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

Generate a summarized description of a body of text

from transformers import pipeline def summarize_text(text): summarizer = pipeline("summarization") summary = summarizer(text, max_length=100, min_length=30, do_sample=False) return summary[0]['summary_text']

A C# utility to summarize a text message up to a predefined max characters.

this bot is made for summarizing the text which min_length is 15 and max_length is 500

Text summarization app (Flask + Transformers). Paste text or call the API to get concise summaries powered by BART large CNN; supports min/max length

The optimize_prompt_text() function takes two arguments: input_text (the text to be optimized) and max_token_count (the maximum number of tokens to retain). Text summarization is performed using the summarize() function from the gensim.summarization module. This function uses the TextRank algorithm to generate a summary of the input text based on

Streamlit app for OCR + LLM workflows: EasyOCR for text extraction, Groq/Hugging Face backends for summarization, entities, and translation. Includes provider toggle and parameter controls (temperature, max_tokens).

A FastAPI-powered PDF summarization API that extracts text from contracts or invoices (max 3 pages), summarizes it using a transformer model, and returns a structured JSON with key entities like dates, payment terms, penalties, and company names. Uses PyMuPDF and Huggingface Transformers.

Build a FastAPI application with 3 endpoints and deploy it.GET /health - Health check (returns status and timestamp) POST /summarize - Takes text and max_length, returns summary POST /analyze-sentiment - Takes text, returns sentiment (positive/negative/neutral), confidence score, and explanation

In the journey of exploring the flied of Data science and predictivemodeling, I explored this Very interesting algorithm Logistic Regression algorithm.I have tried to leverage the ability of the Classification algorithm whichcomes under Supervised learning of Section of predictive modeling. I used the Logistic Regression algorithm for theclassification of approval rate of the projects submitted by the teachers ofUnited states for students.The main business context of the Project was to reduce the manualevaluation of the projects that was done by volunteers as the process of evaluationcan take long time, which may also be biased on some factors and some irreducibleerrors could also be introduced into the processes. Some other import points are.·      How to scale current manual processes andresources to screen 500,000 projects so that they can be posted as quickly andas efficiently as possible·      How to increase the consistency of projectvetting across different volunteers to improve the experience for teachers·      How to focus volunteer time on the applicationsthat need the most assistance.The goal of the Project is to predict whether or not aDonorsChoose.org project proposal submitted by a teacher will be approved,using the text of project descriptions as well as additional metadata about theproject, teacher, and school. DonorsChoose.org can then use this information toidentify projects most likely to need further review before approvalThe steps followed for Data Preparation and PredictiveModeling is as follows:Note: Giving Unstructured data (Garbage in common terms) toa machine learning algorithm gives you random data (Garbage) again. All the code is written in a very clean and untestablemanner ignoring fancy methods where ever possible and reference for everything thatis used in coding is given above the code so that is it easy for everyone tounderstand the code and leverage the potential that AI has, because I believein growing together and helping others as this makes me a great team player . Italso increases the story telling ability and to represent data.For implementation of all the code I have used the SKlearn Library.  1.      Logistic Regression (either SGDClassifierwith log loss, or LogisticRegression) on these feature setsSet 1: categorical, numerical features+ project_title(BOW) + preprocessed_eassay (`BOW with bi-grams` with`min_df=10` and `max_features=5000`)Set 2: categorical, numericalfeatures + project_title(TFIDF)+ preprocessed_eassay (`TFIDF with bi-grams`with `min_df=10` and `max_features=5000`)Set 3: categorical, numericalfeatures + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V)Set 4: categorical, numericalfeatures + project_title(TFIDF W2V)+ preprocessed_essay (TFIDF W2V) 2.      Hyper parameter tuning (find best hyperparameters corresponding the algorithm that you choose)1.      Find the best hyper parameter which will givethe maximum AUC value2.      Find the best hyper parameter using k-fold crossvalidation or simple cross validation data3.      Use gridsearch cv or random search cv or you canalso write your own for loops to do this task of hyperparameter tuning 3.      Representation of results1.      You need to plot the performance of model bothon train data and cross validation data for each hyper parameter, like shown inthe figure. 2.      Once after you found the best hyper parameter,you need to train your model with it, and find the AUC on test data and plotthe ROC curve on both train and test. 3.      Along with plotting ROC curve, you need to printthe confusion matrix with predicted and original labels of test data points.Please visualize your confusion matrices using seaborn heatmaps.  Task-2 Apply Logistic Regression on the belowfeature set Set 5 by finding the best hyper parameter as suggested in step 2and step 3.Consider these set of features Set 5 :school state : categorical dataclean categories : categorical dataclean subcategories : categorical dataproject_grade_category :categorical datateacher prefix : categorical dataquantity : numerical datateacher_number_of_previously_posted_projects : numerical dataprice : numerical datasentiment score's of each of the essay : numerical datanumber of words in the title : numerical datanumber of words in the combine essays : numerical dataAnd apply the Logistic regression on these features byfinding the best hyper paramter as suggested in step 2 and step 3 4 . ConclusionYou need to summarize the results at the end of thenotebook, summarize it in the table format. To print out a table please referto this pretty table library link.