NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

Neural Network Projects with Python, Published by Packt

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Skip to content Search… All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. @giansalex giansalex/torrent-courses-download-list.md forked from M-Younus/torrent courses download-list Last active 2 days ago 15188 Code Revisions 15 Stars 151 Forks 88 <script src="https://gist.github.com/giansalex/4cd3631e94433bbbd71bf07aedb33a7b.js"></script> torrent-courses-download-list.md Torrent Courses List Download http://kickass.to/infiniteskills-learning-jquery-mobile-working-files-t7967156.html http://kickass.to/lynda-bootstrap-3-advanced-web-development-2013-eng-t8167587.html http://kickass.to/lynda-css-advanced-typographic-techniques-t7928210.html http://kickass.to/lynda-html5-projects-interactive-charts-2013-eng-t8167670.html http://kickass.to/vtc-html5-css3-responsive-web-design-course-t7922533.html http://kickass.to/10gen-m101js-mongodb-for-node-js-developers-2013-eng-t8165205.html http://kickass.to/cbt-nuggets-amazon-web-services-aws-foundations-t7839734.html http://kickass.to/cbt-nuggets-apache-hadoop-t8027965.html http://kickass.to/cbt-nuggets-backtrack-and-kali-linux-t7677281.html http://kickass.to/cbt-nuggets-ccda-desgn-640-864-t8300917.html http://kickass.to/cbt-nuggets-ccna-wireless-iuwne-640-722-t8300389.html http://kickass.to/cbt-nuggets-cisco-ccna-labs-cisco-for-the-real-world-bonus-t6154766.html http://kickass.to/cbt-nuggets-cisco-ccnp-security-firewall-v2-0-642-618-azazredhat-t6955696.html http://kickass.to/cbt-nuggets-cisco-ccnp-security-secure-642-637-azazredhat-t6955710.html http://kickass.to/cbt-nuggets-comptia-network-videos-2010-gurufuel-t4648514.html http://kickass.to/cbt-nuggets-definitive-guide-to-working-with-gns3-by-keith-bar-t8301349.html http://kickass.to/cbt-nuggets-ec-council-certified-ethical-hacker-v7-0-t6801120.html http://kickass.to/cbt-nuggets-exam-walkthrough-cisco-icnd1ccent-100-101-t8516719.html http://kickass.to/cbt-nuggets-exam-walkthrough-cisco-icnd2ccna-200-101-t8524803.html http://kickass.to/cbt-nuggets-linux-in-the-real-world-with-shawn-powers-t7718107.html http://kickass.to/cbt-nuggets-linux-series-video-tutorial-t485320.html http://kickass.to/cbt-nuggets-lpi-linux-lpic-1-101-and-comptia-linux-t8031864.html http://kickass.to/cbt-nuggets-lpi-linux-lpic-1-102-and-comptia-linux-t8031871.html http://kickass.to/cbt-nuggets-mastering-vmware-view-5-and-preparing-for-the-vcp510-dt-exam-t8301829.html http://kickass.to/cbt-nuggets-vmware-virtualization-vcp-vsphere-5-t8300512.html http://kickass.to/cbt-nuggets-wireshark-with-keith-barker-t8040855.html http://kickass.to/comptia-network-n10-005-collection-t8319928.html http://kickass.to/developing-in-html5-with-javascript-and-css3-jump-start-t8277565.html http://kickass.to/eli-the-computer-guy-linux-t8647714.html http://kickass.to/foundations-of-programming-test-driven-development-t7522376.html http://kickass.to/infiniteskills-advanced-html5-programming-t7463355.html http://kickass.to/infiniteskills-cisco-ccna-certification-bundle-2013-t7645010.html http://kickass.to/infiniteskills-css3-transformations-and-animations-t7930047.html http://kickass.to/infiniteskills-learning-javascript-programming-t7625039.html http://kickass.to/infiniteskills-learning-python-programming-t7107001.html http://kickass.to/infiniteskills-learning-regular-expressions-t8028765.html http://kickass.to/infiniteskills-learning-whitehat-hacking-and-penetration-testing-t8303725.html http://kickass.to/infiniteskills-microsoft-windows-server-2012-certification-training-exam-70-410-t7379360.html http://kickass.to/infiniteskills-php-security-t8046511.html http://kickass.to/learning-vmware-esxi-and-vsphere-5-1-administration-training-t8030885.html http://kickass.to/linuxcbt-basic-security-edition-d3x-t7650913.html http://kickass.to/linuxcbt-config-mgmt-edition-d3x-t7650929.html http://kickass.to/linuxcbthttpdxil-edition-d3x-t7653897.html http://kickass.to/linuxcbt-vbox-edition-d3x-t7653916.html http://kickass.to/linuxcbt-webscan-edition-d3x-t7653922.html http://kickass.to/linuxcbt-winpython-edition-d3x-t7653942.html http://kickass.to/linuxcbt-xenvm-edition-d3x-t7653948.html http://kickass.to/lynda-com-foundations-of-programming-code-efficiency-t8604312.html http://kickass.to/lynda-com-foundations-of-programming-databases-t8596357.html http://kickass.to/lynda-com-foundations-of-programming-design-patterns-t8692867.html http://kickass.to/lynda-com-foundations-of-programming-fundamentals-t7600288.html http://kickass.to/lynda-com-foundations-of-programming-web-services-including-exercise-files-torrenters-t7797117.html http://kickass.to/lynda-com-ruby-on-rails-4-essential-training-dec-2013-t8438392.html http://kickass.to/lynda-foundations-of-programming-refactoring-code-t7524343.html http://kickass.to/lynda-foundations-of-programming-software-quality-assurance-sum1-here-silverrg-t8043799.html http://kickass.to/lynda-javascript-events-t7893809.html http://kickass.to/lynda-leading-with-emotional-intelligence-t8157240.html http://kickass.to/lynda-management-tips-t8154761.html http://kickass.to/mysql-database-tutorials-by-bucky-thenewboston-org-1-33-t8224550.html http://kickass.to/packtpub-advanced-penetration-testing-for-highly-secured-environments-t8300620.html http://kickass.to/pluralsight-mysql-query-optimization-and-performance-tuning-with-pinal-dave-t8553369.html http://kickass.to/pluralsight-relational-database-design-t8551479.html http://kickass.to/ruby-tutorial-bucky-totally-for-beginner-t8699509.html http://kickass.to/trainsignal-vmware-vcloud-director-5-1-essentials-t7495660.html http://kickass.to/trainsignal-vmware-vsphere-optimize-and-scale-vcap5-dca-t7495659.html http://kickass.to/trainsignal-vmware-workstation-9-for-the-it-admin-t7495658.html http://kickass.to/tutsplus-advanced-command-line-techniques-t7632228.html http://kickass.to/tutsplus-advanced-javascript-fundamentals-t6739742.html http://kickass.to/tutsplus-agile-design-patterns-2012-t6992118.html http://kickass.to/tutsplus-cleaner-code-with-coffeescript-t6741625.html http://kickass.to/tutsplus-detecting-code-smells-t8128341.html http://kickass.to/tutsplus-firebug-white-to-black-belt-v413hav-t7154501.html http://kickass.to/tutsplus-foundational-flask-creating-your-own-static-blog-generator-t8356996.html http://kickass.to/tutsplus-freelance-bootcamp-t6832678.html http://kickass.to/tutsplus-premium-e-book-mega-pack-v413hav-t7178526.html http://kickass.to/tutsplus-pro-workflow-for-web-designers-t6854268.html http://kickass.to/tutsplus-riding-ruby-on-rails-t6728201.html http://kickass.to/tutsplus-sql-essentials-t6746851.html http://kickass.to/tutsplus-tools-of-the-modern-web-developer-t8107617.html http://kickass.to/tutsplus-video-fundamentals-t6752217.html http://kickass.to/ine-ccna-wireless-640-722-iuwne-t8301376.html http://kickass.to/learn-metasploit-t8174472.html http://kickass.to/lynda-ruby-on-rails-essential-training-t7630711.html http://kickass.to/lynda-up-and-running-with-python-2013-eng-t8167709.html http://kickass.to/build-flat-responsive-website-from-scratch-complete-course-t8604527.html http://kickass.to/canvas-essentials-t8550909.html http://kickass.to/cbt-nuggets-70-331-microsoft-sharepoint-server-2013-x264-mkv-encod3r-t8595423.html http://kickass.to/cbt-nuggets-98-365-windows-server-admin-fundamentals-encod3r-t8613009.html http://kickass.to/cbt-nuggets-ccie-combo-pack-t271107.html http://kickass.to/cbt-nuggets-ccna-certification-videos-material-2010-gurufu-t4648321.html http://kickass.to/cbt-nuggets-juniper-networks-certified-specialist-security-jncis-sec-jn0-332-t8028083.html http://kickass.to/cehv7-cbt-nuggets-instructor-slides-tools-video-tools-study-guide-rar-t8705752.html http://kickass.to/cisco-ccna-initial-router-and-switch-configuration-t8648377.html http://kickass.to/cisco-ccna-security-aaa-and-ip-security-t8648378.html http://kickass.to/cisco-ccna-security-introduction-to-network-security-t8648381.html http://kickass.to/cisco-ccna-voice-configuration-and-advanced-features-t8648387.html http://kickass.to/cisco-ccna-voice-voice-overview-and-lab-setup-t8648386.html http://kickass.to/cisco-press-ccna-security-640-554-official-cert-guide-videos-t8648384.html http://kickass.to/coursera-neural-networks-and-machine-learning-geoffrey-hinton-university-of-toronto-t8568642.html http://kickass.to/eli-the-computer-guy-hacking-t8647661.html http://kickass.to/ine-ccie-data-center-storage-t8029396.html http://kickass.to/infinite-skills-learning-cloud-computing-with-amazon-web-services-2013-eng-t8703045.html http://kickass.to/infiniteskills-learning-tcp-ip-t8303739.html http://kickass.to/lynda-bootstrap-3-new-features-and-migration-t7958409.html http://kickass.to/lynda-bootstrap-adding-interactivity-to-your-site-t7519306.html http://kickass.to/lynda-com-jquery-ui-widgets-t8172743.html http://kickass.to/lynda-essential-training-t8157222.html http://kickass.to/lynda-foundation-incorporating-sass-and-compass-t7953037.html http://kickass.to/lynda-html5-projects-advanced-to-do-list-t7855578.html http://kickass.to/lynda-html5-projects-creating-a-responsive-presentation-2013-eng-t8167660.html http://kickass.to/lynda-online-presentations-with-reveal-js-2013-eng-t8167575.html http://kickass.to/lynda-teacher-tips-t8157202.html http://kickass.to/lynda-up-and-running-with-angularjs-t7982840.html http://kickass.to/lynda-up-and-running-with-bootstrap-3-t8011198.html http://kickass.to/lynda-up-and-running-with-cakephp-t7963854.html http://kickass.to/lynda-up-and-running-with-google-apps-script-t7917458.html http://kickass.to/lynda-up-and-running-with-php-codeigniter-t7849968.html http://kickass.to/lynda-web-semantics-t7899223.html http://kickass.to/lynda-wordpress-essential-training-2013-tutorial-t8270624.html http://kickass.to/pluralsight-aws-developer-fundamentals-2013-eng-t8703013.html http://kickass.to/pluralsight-bootstrap-3-t8214168.html http://kickass.to/pluralsight-cisco-ccie-routing-and-switching-implement-bgp-t8648391.html http://kickass.to/pluralsight-cisco-ccna-advanced-ethernet-and-file-management-t8051456.html http://kickass.to/pluralsight-cisco-ccna-security-firewalls-and-vpns-t8648393.html http://kickass.to/pluralsight-cisco-ccna-wan-technologies-learn-wide-area-network-wan-technologies-and-configuration-t7882351.html http://kickass.to/pluralsight-javascript-from-scratch-t7612372.html http://kickass.to/pluralsight-sublime-text-3-from-scratch-2013-eng-t8153034.html http://kickass.to/ten-ton-wordpress-mastery-video-t8452016.html http://kickass.to/trainsignal-microsoft-network-monitoring-t8028791.html http://kickass.to/tuts-plus-2013-bdd-in-rails-psiclone-t8474590.html http://kickass.to/tutsplus-advanced-css3-animations-t7791566.html http://kickass.to/tutsplus-an-introduction-to-node-js-t6744596.html http://kickass.to/tutsplus-better-statistics-with-google-charts-t7983386.html http://kickass.to/tutsplus-bootstrap-for-web-design-t8210956.html http://kickass.to/tutsplus-com-advanced-ui-techniques-2013-t7072722.html http://kickass.to/tutsplus-com-build-a-cms-in-codeigniter-2013-t7072644.html http://kickass.to/tutsplus-com-learning-mongodb-2013-t7072653.html http://kickass.to/tutsplus-computer-networks-distilled-v413hav-t7630795.html http://kickass.to/tutsplus-css-3d-essentials-t8027191.html http://kickass.to/tutsplus-css-noob-to-ninja-v413hav-t7475010.html http://kickass.to/tutsplus-css-tips-and-tricks-t8292119.html http://kickass.to/tutsplus-css3-essentials-t6608214.html http://kickass.to/tutsplus-css3-typography-techniques-t7882076.html http://kickass.to/tutsplus-design-patterns-in-ruby-t8354740.html http://kickass.to/tutsplus-fundamentals-of-design-t6645691.html http://kickass.to/tutsplus-fundamentals-of-print-design-t6667261.html http://kickass.to/tutsplus-fundamentals-of-ux-design-t6710443.html http://kickass.to/tutsplus-html-kickstart-essentials-t7969388.html http://kickass.to/tutsplus-html-tips-and-tricks-t8224648.html http://kickass.to/tutsplus-introduction-to-web-typography-t6662386.html http://kickass.to/tutsplus-javascript-fundamentals-101-t6738976.html http://kickass.to/tutsplus-jquery-ui-101-the-essentials-2013-eng-t8165125.html http://kickass.to/tutsplus-jquery-ui-101-the-essentials-t7791579.html http://kickass.to/tutsplus-jquery-ui-201-beyond-the-basics-t7791583.html http://kickass.to/tutsplus-jquery-ui-301-the-widget-factory-2013-eng-t8165109.html http://kickass.to/tutsplus-jquery-ui-301-the-widget-factory-working-files-2013-eng-t8180547.html http://kickass.to/tutsplus-laravel-essentials-t6722386.html http://kickass.to/tutsplus-logo-design-fundamentals-with-gary-simon-swatiate-t7867377.html http://kickass.to/tutsplus-mastering-corporate-design-v413hav-t7586047.html http://kickass.to/tutsplus-mastering-flat-design-v413hav-t7781777.html http://kickass.to/tutsplus-mastering-retro-web-design-v413hav-t7343186.html http://kickass.to/tutsplus-object-oriented-javascript-t6863065.html http://kickass.to/tutsplus-perfect-workflow-in-sublime-text-2-t6794850.html http://kickass.to/tutsplus-php-fundamentals-t6671312.html http://kickass.to/tutsplus-php-security-pitfalls-t7835091.html http://kickass.to/tutsplus-relational-databases-t8023530.html http://kickass.to/tutsplus-responsive-web-design-for-beginners-v413hav-t7385876.html http://kickass.to/tutsplus-responsive-web-design-techniques-t8103476.html http://kickass.to/tutsplus-responsive-web-design-with-foundation-t8103477.html http://kickass.to/tutsplus-simple-development-with-jquery-mobile-t6735499.html http://kickass.to/tutsplus-solid-design-patterns-t8208974.html http://kickass.to/tutsplus-test-driven-php-in-action-t6851704.html http://kickass.to/tutsplus-testing-tricks-for-php-and-laravel-developers-t7844807.html http://kickass.to/tutsplus-web-form-design-and-development-t8020800.html http://kickass.to/tutsplus-wordpress-plugin-development-essentials-t6615050.html http://kickass.to/udemy-build-an-instantly-updating-dynamic-website-with-jquery-ajax-t8415746.html http://kickass.to/udemy-psd-to-html5-css3-hand-code-a-beautiful-website-in-4-hours-t7740752.html http://kickass.to/video2brain-drupal-power-workshop-t6811365.html http://kickass.to/video2brain-exploring-css-positioning-t6683727.html http://kickass.to/video2brain-getting-started-with-joomla-t6600909.html http://kickass.to/video2brain-html5-for-beginners-learn-by-video-t6686185.html http://kickass.to/video2brain-html5-power-workshop-t6689166.html http://kickass.to/video2brain-php-5-3-advanced-web-application-programming-t6681560.html http://kickass.to/vtc-mysql-5-development-part-1-of-2-t7502575.html http://kickass.to/vtc-mysql-5-development-part-2-of-2-t7502576.html https://thepiratebay.se/torrent/6113010/Linux_CBT_Scripting_BASH__PERL__PYTHON__PHP https://thepiratebay.se/torrent/7667241/CBT.Nuggets.Python.Programming.Python.Language-PLATO https://thepiratebay.se/torrent/8608894/InfiniteSkills_-_Web_Programming_With_Python https://thepiratebay.se/torrent/7838122/Lynda.com_-_Python_3_Essential_Training https://thepiratebay.se/torrent/7837732/python_book_collection https://thepiratebay.se/torrent/9549614/Pluralsight.com_-_Python_Fundamentals https://thepiratebay.se/torrent/5134755/LiveLessons.Python.Fundamentals.DVDR-HELL https://thepiratebay.se/torrent/7112525/The_New_Boston_-_Python_Programming_Tutorials http://kickass.to/lynda-up-and-running-with-python-2013-eng-t8167709.html http://www.seedpeer.me/details/5730405/CBT-Nuggets---COMPTIA-SECURITY-SY0-201-WITH-SY0-301,-JK0-018-UPDATES.html http://www.seedpeer.me/details/6411686/CBT.Nuggets----IPv6.html http://www.seedpeer.me/details/6421814/CBT-Nuggets---Ubuntu.html http://www.seedpeer.me/details/6107414/LinuxCBT.Awk.Sed.Edition.html http://www.seedpeer.me/details/6107522/LinuxCBT-BASH-II-Edition-d3x.html http://www.seedpeer.me/details/4799869/LinuxCBT---Berkeley-Packet-Filters-BPF-Edition.html http://www.seedpeer.me/details/6881816/LinuxCBT--HTTPD-Edition.html http://www.seedpeer.me/details/6559038/LinuxCBT-Key-Files-edition.html http://www.seedpeer.me/details/6107600/LinuxCBT.MemCacheD.Edition-d3x.html http://www.seedpeer.me/details/5870507/LinuxCBT-Monitoring-Edition-feat-Nagios.html http://www.seedpeer.me/details/6107677/LinuxCBT-NIDS-Edition-d3x.html http://www.seedpeer.me/details/5925487/LinuxCBT-OpenLDAP-Edition.html http://www.seedpeer.me/details/6107558/LinuxCBT.OpenPGP.Edition-d3x.html http://www.seedpeer.me/details/6107692/LinuxCBT-OpenSSHv2-Edition-d3x.html http://www.seedpeer.me/details/6107699/LinuxCBT-PDNS-Edition-d3x.html http://www.seedpeer.me/details/2595080/LinuxCBT-Proxy-Edition-Feat-Squid-AG-torrent-[twistedtorrents2-com].html http://www.seedpeer.me/details/6110590/LinuxCBT-Samba-Edition-d3x.html http://www.seedpeer.me/details/6110595/LinuxCBT-SELinux-Edition-d3x.html http://www.seedpeer.me/details/4799871/LinuxCBT---SFTP-Edition.html http://www.seedpeer.me/details/6110602/LinuxCBT-SQLite-Edition-d3x.html http://www.seedpeer.me/details/5408265/LinuxCBT---Ubuntu-12.04-LTS.html http://www.seedpeer.me/details/4799857/LinuxCBT---UnixCBT-BSD8x-Edition-FreeBSD-8.2.html http://www.seedpeer.me/details/6110504/LinuxCBT.WinPerl.Edition-d3x.html http://www.seedpeer.me/details/6562861/Lynda-com---CMS-Fundamentals.html http://www.seedpeer.me/details/5247098/Lynda.com---Creating-an-Effective-Resume.html http://www.seedpeer.me/details/4989808/Lynda.com---CSS-with-LESS-and-SASS.html http://www.seedpeer.me/details/5340566/Lynda.com---Fundamentals-of-Software-Version-Control-Nov.-2012.html http://www.seedpeer.me/details/5569955/Lynda.com-GMail-For-Power-Users-V413HAV.html http://www.seedpeer.me/details/4631148/Lynda.com-Invaluable-Becoming-a-Leading-Authority.html http://www.seedpeer.me/details/4631108/Lynda.com-Invaluable-Building-Professional-Connections.html http://www.seedpeer.me/details/4623697/Lynda.com---Managing-a-Hosted-Website.html http://www.seedpeer.me/details/5236946/Lynda.com---PayPal-Essential-Training.html http://www.seedpeer.me/details/4596519/Lynda.com---PostgreSQL-9-With-PHP-Essential-Training-iRONiSO.html http://www.seedpeer.me/details/5016023/Lynda.com---Ruby-Essential-Training-with-Kevin-Skoglund.html http://www.seedpeer.me/details/4931186/Lynda.com---Using-Regular-Expressions.html http://www.seedpeer.me/details/6675342/Lynda---Git-Essential-Training.html http://www.seedpeer.me/details/6698556/Lynda---Leading-Change.html http://www.seedpeer.me/details/6973932/PluralSight-Refactoring-Fundamentals.html http://www.seedpeer.me/details/6661700/Tutsplus---Building-Ribbit-in-Rails.html http://www.seedpeer.me/details/6101172/Tutsplus---Cross-Platform-Browser-Testing-V413HAV.html http://www.seedpeer.me/details/5266314/TutsPlus---Git-Essentials.html http://www.seedpeer.me/details/4848412/TutsPlus---How-to-Be-a-Terminal-Pro.html http://www.seedpeer.me/details/4848374/TutsPlus---How-To-Customize-Your-Terminal.html http://www.seedpeer.me/details/4848299/TutsPlus---Maintainable-CSS-With-Sass-and-Compass.html http://www.seedpeer.me/details/4856068/TutsPlus---Regular-Expressions---Up-and-Running.html http://www.seedpeer.me/details/4816386/TutsPlus---The-Fundamentals-of-Ruby.html http://www.seedpeer.me/details/4848281/TutsPlus---The-Ultimate-Guide-for-Learning-Mootools.html http://www.seedpeer.me/details/4935147/CBT-Nuggets---Intermediate-to-Advanced-Linux-Series.html http://www.seedpeer.me/details/6251428/CBT-Nuggets---IPv6gidbcn.html http://www.seedpeer.me/details/5124174/CBT-Nuggets---LINUX-SERIES.html http://www.seedpeer.me/details/2891954/LinuxCBT-Deb5x-Edition-DVD-YUM.html http://www.seedpeer.me/details/4799921/LinuxCBT---Enterprise-Linux-4-Edition.html http://www.seedpeer.me/details/6290791/LinuxCBT-Network-Intrusion-Detection-System.html http://www.seedpeer.me/details/6107569/LinuxCBT.PackCapAnal.Edition-d3x.html http://www.seedpeer.me/details/6107588/LinuxCBT.PAM.Edition-d3x.html http://www.seedpeer.me/details/6110616/LinuxCBT-Win-Awk-Sed-Edition-d3x.html http://www.seedpeer.me/details/6666824/Packtpub-BackTrack-5-Wireless-Penetration-Testing-[Video].html http://www.seedpeer.me/details/6668649/Packtpub-Getting-started-with-Apache-Solr-Search-Server-[Video].html http://www.seedpeer.me/details/6668652/Packtpub-Getting-Started-with-Citrix-XenApp-6.5-[Video].html http://www.seedpeer.me/details/6668669/Packtpub-Kali-Linux---Backtrack-Evolved-Assuring-Security-by-Penetration-Testing.html http://www.seedpeer.me/details/6415199/Pluralsight-com-Installing-and-Configuring-Apache-Web-Server-iNKiSO.html http://www.seedpeer.me/details/6271468/Pluralsight---MySQL-Indexing-for-Performance-2013.html http://www.seedpeer.me/details/6228283/Pluralsight---Web-Performance-Course.html http://www.seedpeer.me/details/6376899/TutsPlus---Documentation-in-Ruby.html http://www.seedpeer.me/details/5661723/CBT-Nuggets-&acirc;%EF%BF%BD%EF%BF%BD-Cisco-CCENT-CCNA-ICND1-100-101.html http://www.seedpeer.me/details/5825975/CBT-Nuggets-CCNA-200-101-mp4.html http://www.seedpeer.me/details/5513622/CBT-Nuggets---Cisco-CCNA-Security-640-554.html http://www.seedpeer.me/details/5890097/CBT-Nuggets---Citrix-XenApp-6.5.html http://www.seedpeer.me/details/6187994/CBT-Nuggets----CompTIA-A-220-801-&amp;-220-802-Update-2012-iso.html http://www.seedpeer.me/details/6353101/CBT-Nuggets---CompTIA-Security.rar.html http://www.seedpeer.me/details/5243830/CBT-Nuggets---Oracle-Certified-Professional-1Z0-053-OCP.html http://www.seedpeer.me/details/4935122/CBT-Nuggets---Oracle-Database-11g-DBA-1-1Z0-052.html http://www.seedpeer.me/details/7222524/CBT.Nuggets----Oracle.Database.11G.DBA.1Z0-053-EnCod3r.html http://www.seedpeer.me/details/4935128/CBT-Nuggets---Oracle-Database-11g-SQL-Fundamentals-1-1Z0-051.html http://www.seedpeer.me/details/5863952/CBTNuggets-VMware-View-5.iso.html http://www.seedpeer.me/details/6199576/CBT-Nuggets---Web-Development.html http://www.seedpeer.me/details/4825729/LinuxCBT---CentOS6x-Edition.html http://www.seedpeer.me/details/1520287/linuxCBT---DBMS-mysql-5-Training.html http://www.seedpeer.me/details/4799864/LinuxCBT---Deb6x-Edition.html http://www.seedpeer.me/details/4799881/LinuxCBT---Debian-Edition.html http://www.seedpeer.me/details/1548037/LINUXCBT-FEAT-SUSE-10-ENTERPRISE-EDITION-JGTiSO[www.thepeerhub.com].html http://www.seedpeer.me/details/6107551/LinuxCBT-KornShell-Edition-d3x.html http://www.seedpeer.me/details/4261635/Linuxcbt-Redhat-6-Enterprise-Tutorials.html http://www.seedpeer.me/details/1662106/LinuxCBT---RHEL5.html http://www.seedpeer.me/details/6110601/LinuxCBT-SLES-10-Edition-d3x.html http://www.seedpeer.me/details/4799923/LinuxCBT---SLES-11-Edition-SUSE-11-Enterprise.html http://www.seedpeer.me/details/6964916/Lynda---ASP.NET-MVC-4-Essential-Training.html http://www.seedpeer.me/details/7253647/Lynda---Building-Facebook-Applications-with-PHP-and-MySQL.html http://www.seedpeer.me/details/5552857/Lynda.com---Applied-Responsive-Design-Mar,-2013.html http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html http://www.seedpeer.me/details/4986911/Lynda.com---C&amp;C-Essential-Training.html http://www.seedpeer.me/details/4504272/Lynda.com-Choosing-Using-Web-Fonts.html http://www.seedpeer.me/details/6554622/Lynda.com---Designing-Resume.html http://www.seedpeer.me/details/5332552/Lynda.com---Drupal-7-Advanced-Training---TestOrToast.html http://www.seedpeer.me/details/7051972/Lynda.com---Drupal-7--Creating-and-Editing-Custom-Themes---with-Chaz-Chumley[Isaac-9].html http://www.seedpeer.me/details/5565633/Lynda.com---JavaScript-and-JSON-Mar,-2013.html http://www.seedpeer.me/details/6664728/Lynda.com-JavaScript-for-Web-Designers[2013].html http://www.seedpeer.me/details/6664733/Lynda.com-Node.js-Essential-Training[2013].html http://www.seedpeer.me/details/4591597/Lynda.com---Practical-and-Effective-JavaScript.html http://www.seedpeer.me/details/5256920/Lynda.com-Responsive-Design-with-Joomla--Exercice-Files.html http://www.seedpeer.me/details/5374680/Lynda.com---Simplified-Drupal-Sites-with-Drush---TestOrToast.html http://www.seedpeer.me/details/4795822/Lynda.com---Unix-for-Mac-OS-X-Users.html http://www.seedpeer.me/details/6716808/[Lynda.com]-Up-and-Running-with-Amazon-Web-Services-[2013,-ENG].html http://www.seedpeer.me/details/4593746/Lynda.com-Web-Form-Design-Best-Practices.html http://www.seedpeer.me/details/4850397/Lynda---Create-Your-First-Online-Store-with-Drupal-Commerce.html http://www.seedpeer.me/details/4850389/Lynda---Drupal-7-:-Essential-Training.html http://www.seedpeer.me/details/4850540/Lynda---Drupal-7-New-Features.html http://www.seedpeer.me/details/4850393/Lynda---Drupal-7-:-Reporting-and-Visualizing-Data.html http://www.seedpeer.me/details/5996422/Lynda---Up-and-Running-with-Backbone.js.html http://www.seedpeer.me/details/6971211/Lynda---Up-and-Running-with-CakePHP.html http://www.seedpeer.me/details/6666828/Packtpub-Beginning-Yii-[Video].html http://www.seedpeer.me/details/6666832/Packtpub-Building-a-Website-with-Drupal-[Video].html http://www.seedpeer.me/details/6668107/Packtpub-Drupal-7-Module-Development-[Video].html http://www.seedpeer.me/details/6668679/Packtpub-Learning-Joomla-3-Extension-Development-[Video].html http://www.seedpeer.me/details/7101071/Pluralsight---AngularJS-Fundamentals-[OGNADROL].html http://www.seedpeer.me/details/7268422/[Pluralsight]-AWS-Developer-Fundamentals-[2013,-ENG].html http://www.seedpeer.me/details/6695354/Pluralsight---Beginning-HTML5-Game-Development-With-Quintus.html http://www.seedpeer.me/details/6370939/Pluralsight---Cisco-CCNA-WAN-Technologies---Learn-wide-area-network-WAN-technologies-and-configuration.html http://www.seedpeer.me/details/6383616/Pluralsight-Introduction-to-Spring-MVC2013.html http://www.seedpeer.me/details/6228297/Pluralsight---Introduction-to-the-Facebook-Graph-API.html http://www.seedpeer.me/details/6294391/Pluralsight---Optimizing-and-Managing-Distributed-Systems-on-AWS-2013.html http://www.seedpeer.me/details/6698563/[Pluralsight]-Sublime-Text-3-From-Scratch-[2013,-ENG].html http://www.seedpeer.me/details/5056370/Tutsplus---Advanced-Backbone-Patterns-and-Techniques-2012.html http://www.seedpeer.me/details/7233352/Tutsplus---Become-a-Professional-JavaScript-Developer-Basics.html http://www.seedpeer.me/details/4848277/TutsPlus---Build-Web-Apps-in-Node-and-Express.html http://www.seedpeer.me/details/5683153/Tutsplus---Catch-Up-with-Ruby-on-Rails-4.html http://www.seedpeer.me/details/4918947/TutsPlus---CodeIgniter-Essentials.html http://www.seedpeer.me/details/5069781/TutsPlus---Connected-to-the-Backbone.html http://www.seedpeer.me/details/5513056/Tutsplus---Designing-Professional-Resumes.html http://www.seedpeer.me/details/5706815/Tutsplus-Easier-JavaScript-Apps-with-AngularJS.html http://www.seedpeer.me/details/6462415/TutsPlus---Easier-JavaScript-with-TypeScript.html http://www.seedpeer.me/details/5868293/TutsPlus---Getting-Started-With-Windows-8-Development-Using-HTML,-CSS-&amp;-JavaScript-V413HAV.html http://www.seedpeer.me/details/6150521/TutsPlus-HTML5-Video-Essentials-PRODEV.html http://www.seedpeer.me/details/4841911/TutsPlus---JavaScript-Testing-With-Jasmine.html http://www.seedpeer.me/details/6593486/TutsPlus---Less-is-More.html http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

In this project we utilize OpenCV t in order to identify the license number plates and the python pytesseract for the characters and digits extraction from the plate. As well this project will presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. We build Web App with a Python program that automatically recognizes the License Number Plate by the end of this journi. The results have shown that the trained neural network is able to perform with high accuracy of nearly 90-95 percent in recognizing license plates in low resolution images using this system.

Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree

A complete course on AI and machine learning, featuring Python-based tutorials, projects, and datasets covering algorithms, neural networks, and real-world applications. Designed for beginners to advanced learners, with hands-on exercises in Scikit-learn, TensorFlow, and PyTorch.

Tutorials on how to engineer Machine Learning projects using Deep Neural Networks with PyTorch and Python

Tesseract is a cross-OS optical character recognition (OCR) engine developed by HP in the 1980s, and since 2006, maintained by Google as an open-source project with high marks for accuracy in reading raw image data into digital characters. The project has been continuously developed and now offers OCR supported by LSTM neural networks for highly improved results. In this session, we’ll use the Python wrapper for Tesseract to first test drive OCR on images through code before connecting our solution to a live IP video feed from your smartphone processed through OpenCV, and then translating the resultant text stream into audible form with gTTS (Google Text-To-Speech), enabling our mashup program to automatically read out loud from any script it ‘sees’. Prerequisites: —Python IDE such as PyCharm (https://www.jetbrains.com/pycharm) —The Tesseract engine (https://tesseract ocr.github.io/tessdoc/Home.html) —A smartphone configured as an IP Webcam (https://www.makeuseof.com/tag/ip-webcam-android-phone-as-a-web-cam/) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #OCR #TextToSpeech #Tesseract

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI. Created by: Deeplearning.AI

Authentication is a significant issue in system control in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This project describes a method for Student’s Attendance System which will integrate with the face recognition technology using deep learning algorithms. The system will recognize the students present in the classroom and provide the list of present students for the lecture. The primary technique used for the face detection is by using python inbuilt packages of OpenCV. Once the model is trained on different kinds of datasets, the project will help in identifying students present for the class. The front end will be based on an android application. The application uses SQLite database for establishing connection between web app and the model. The backend model mainly comprises of a convolutional neural network which extracts features and trains the model in recognizing those features. The inbuilt OpenCV uses haarcascade classifiers in identifying the faces present in the input image. The list of identified will be displayed as the end result.

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

Language Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Get the Data Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus. """ DON'T MODIFY ANYTHING IN THIS CELL """ import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) Explore the Data Play around with view_sentence_range to view different parts of the data. view_sentence_range = (0, 10) """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) Dataset Stats Roughly the number of unique words: 227 Number of sentences: 137861 Average number of words in a sentence: 13.225277634719028 English sentences 0 to 10: new jersey is sometimes quiet during autumn , and it is snowy in april . the united states is usually chilly during july , and it is usually freezing in november . california is usually quiet during march , and it is usually hot in june . the united states is sometimes mild during june , and it is cold in september . your least liked fruit is the grape , but my least liked is the apple . his favorite fruit is the orange , but my favorite is the grape . paris is relaxing during december , but it is usually chilly in july . new jersey is busy during spring , and it is never hot in march . our least liked fruit is the lemon , but my least liked is the grape . the united states is sometimes busy during january , and it is sometimes warm in november . French sentences 0 to 10: new jersey est parfois calme pendant l' automne , et il est neigeux en avril . les états-unis est généralement froid en juillet , et il gèle habituellement en novembre . california est généralement calme en mars , et il est généralement chaud en juin . les états-unis est parfois légère en juin , et il fait froid en septembre . votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme . son fruit préféré est l'orange , mais mon préféré est le raisin . paris est relaxant en décembre , mais il est généralement froid en juillet . new jersey est occupé au printemps , et il est jamais chaude en mars . notre fruit est moins aimé le citron , mais mon moins aimé est le raisin . les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre . Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. This will help the neural network predict when the sentence should end. You can get the <EOS> word id by doing: target_vocab_to_int['<EOS>'] You can get other word ids using source_vocab_to_int and target_vocab_to_int. def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) """ # TODO: Implement Function source_id_text = [[source_vocab_to_int[word] for word in sentence.split()] \ for sentence in source_text.split('\n')] target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int['<EOS>']] \ for sentence in target_text.split('\n')] return source_id_text, target_id_text """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_text_to_ids(text_to_ids) Tests Passed Preprocess all the data and save it Running the code cell below will preprocess all the data and save it to file. """ DON'T MODIFY ANYTHING IN THIS CELL """ helper.preprocess_and_save_data(source_path, target_path, text_to_ids) Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. import problem_unittests as tests """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU """ DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) TensorFlow Version: 1.1.0 Default GPU Device: /gpu:0 Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: model_inputs process_decoder_input encoding_layer decoding_layer_train decoding_layer_infer decoding_layer seq2seq_model Input Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: Input text placeholder named "input" using the TF Placeholder name parameter with rank 2. Targets placeholder with rank 2. Learning rate placeholder with rank 0. Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0. Target sequence length placeholder named "target_sequence_length" with rank 1 Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0. Source sequence length placeholder named "source_sequence_length" with rank 1 Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], 'input') targets = tf.placeholder(tf.int32, [None, None]) learning_rate = tf.placeholder(tf.float32, []) keep_prob = tf.placeholder(tf.float32, [], 'keep_prob') target_sequence_length = tf.placeholder(tf.int32, [None], 'target_sequence_length') max_target_len = tf.reduce_max(target_sequence_length) source_sequence_length = tf.placeholder(tf.int32, [None], 'source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_sequence_length, max_target_len, source_sequence_length """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_model_inputs(model_inputs) Tests Passed Process Decoder Input Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch. def process_decoder_input(target_data, target_vocab_to_int, batch_size): """ Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data """ # TODO: Implement Function go = tf.constant([[target_vocab_to_int['<GO>']]]*batch_size) # end = tf.slice(target_data, [0, 0], [-1, batch_size]) end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([go, end], 1) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_process_encoding_input(process_decoder_input) Tests Passed Encoding Implement encoding_layer() to create a Encoder RNN layer: Embed the encoder input using tf.contrib.layers.embed_sequence Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper Pass cell and embedded input to tf.nn.dynamic_rnn() from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) """ # TODO: Implement Function embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) def lstm_cell(): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) return tf.contrib.rnn.DropoutWrapper(lstm, keep_prob) stacked_lstm = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)]) # initial_state = stacked_lstm.zero_state(source_sequence_length, tf.float32) return tf.nn.dynamic_rnn(stacked_lstm, embed, source_sequence_length, dtype=tf.float32) # initial_state=initial_state) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) Tests Passed Decoding - Training Create a training decoding layer: Create a tf.contrib.seq2seq.TrainingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id """ # TODO: Implement Function helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_train_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) # for tensorflow 1.2: # dec_train_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) return dec_train_logits # keep_prob/dropout not used? """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_train(decoding_layer_train) Tests Passed Decoding - Inference Create inference decoder: Create a tf.contrib.seq2seq.GreedyEmbeddingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id """ # TODO: Implement Function start_tokens = tf.constant([start_of_sequence_id]*batch_size) helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_infer_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) # for tensorflow 1.2: # dec_infer_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) return dec_infer_logits """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_infer(decoding_layer_infer)

With recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.

This project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2].  ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by chenleinj@njust.edu.cn ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .

 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── base_deepfake (1) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── both_deepfake (3) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │   ├── DeepFakeDetection │   ├── Deepfakes │   ├── Face2Face │   ├── FaceSwap │   └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.

Using-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.

Project made with python to predict human emotional expressions given images of people's faces using Deep Neural Networks.

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

Final year project: E-Commerce system with object recognition using Neural Network (AI) using Python, Django, JavaScript, MySQL, HTML, CSS, Bootstrap. The system gives the ability to the user to register and login to the system as well as search the products by typing or image at a search bar of website. In addition, the sellers can access the system to add the new products, edit and delete with small dashboard

Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.

This project aims to demonstrate how to create a neuro-fuzzy network using Python. We can use the Keras library, which provides a convenient interface for building and training neural networks, and the skfuzzy module, which provides functions for working with fuzzy logic..

Wine Quality Prediction using machine learning with python .i did this project in AINN(Artificial Intelligence and Neural Network) course .in this project i used red and white wine databases and machine learning libraries available in python

This project is based on analysis and classification of news using an LSTM (Long Short Term Memory) - Recurrent Neural Network to Identify fake news over a text-based news stream.Developed using Python, Tensorflow, pandas with spacy, Wordcloud, and gensim for Natural Language Processing and seaborn, plotly for visualizations.

The project deals with Detecting skin diseases based on images. The model has been implemented using Python and Convolutional Neural Networks and OpenCV. The approach works on color images and greyscale images. Used different Neural Network layers such as Max-Pooling, Flatten, Conv2D, etc. to build a system that successfully detects skin diseases based on images captured through camera and deployed model using flask application and web development technologies. Received Silver Award at Ennovate-The International Innovation Show-2021, Poland for this innovation

This project uses EEG data to detect epileptic seizures with machine learning models, focusing on CNN and RNN architectures. It includes preprocessing, feature extraction, and model evaluation, leveraging Python, TensorFlow/Keras, and scikit-learn for implementation. The aim is to enhance seizure prediction through neural network-based analysis.

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease. This project will utilize a dataset of 303 patients and distributed by the UCI Machine Learning Repository. Machine learning and artificial intelligence is going to have a dramatic impact on the health field; as a result, familiarizing yourself with the data processing techniques appropriate for numerical health data and the most widely used algorithms for classification tasks is an incredibly valuable use of your time! In this tutorial, we will do exactly that. We will be using some common Python libraries, such as pandas, numpy, and matplotlib. Furthermore, for the machine learning side of this project, we will be using sklearn and keras.

Object detection and identification is one of the most important and challenging branches of computer vision, which has been widely applied in peoples’ life, such as monitoring security, autonomous driving, and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. By using Machine Learning and ResNet, we can easily identify the names of the objects which we needed. For this, firstly the training data is fed to the machine and labeled it correctly based on the nomenclature. By using the Camera Module, the test data is detected and verified with the train data using the ResNet algorithm. By repeated testing of the objects, the data set is updated or deleted based on the errors made by the machine in identification. On the repeated iteration of identifying the objects correctly ie., Accuracy reaching ≥ 95%, the dataset, and the application is used in Real World for automation. For this, I use Keras, an open-source neural-network library written in Python and by using the IoT module, the identified data is transferred to the Display device wirelessly. In Real-Time, this project is used for the identification of objects with more than 95% accuracy and transmit the data from anywhere and anytime using the cloud, and completely automate the process and reduces the manpower. ESP32 : Engineered for mobile devices, wearable electronics and IoT applications, ESP32 achieves ultra-low power consumption with a combination of several types of proprietary software. ESP32 also includes state-of-the-art features, such as fine-grained clock gating, various power modes and dynamic power scaling.