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

A Chatbot of Data Science Expert- Chat with Document(s) using ChatGPT API and Text Embedding

GENARATED BY NISHEN Mtroid whatsApp bot 🪀 Command:`setup `✨️ Description:` edit bot settings `⚠️️ Warn `🪀 Command:` install <br> `✨️ Description:` Install external plugins. <br> `⚠️️ Warn:` Get plugins only from https://t.me/AlphaXplugin. `🪀 Command:` plugin<br> `✨️ Description:` Shows the plugins you have installed. `🪀 Command:` remove<br> `✨️ Description:` Removes the plugin. `🪀 Command:` admin<br> `✨️ Description:` Admin menu. `🪀 Command:` ban <br> `✨️ Description:` Ban someone in the group. Reply to message or tag a person to use command. `🪀 Command:` gname <br> `✨️ Description:` Change group name. `🪀 Command:` gdesc<br> `✨️ Description:` Change group discription. `🪀 Command:` dis <br> `✨️ Description:`  Disappearing message on/off. <br> `💡 Example:` .dis on/off `🪀 Command:` reset<br> `✨️ Description:` Reset group invitation link. `🪀 Command:` gpp<br> `✨️ Description:` Set group profile picture `🪀 Command:` add<br> `✨️ Description:` Adds someone to the group. `🪀 Command:` promote <br> `✨️ Description:` Makes any person an admin. `🪀 Command:` demote <br> `✨️ Description:` Takes the authority of any admin. `🪀 Command:` mute <br> `✨️ Description:` Mute the group chat. Only the admins can send a message. ⌨️ Example: .mute & .mute 5m etc `🪀 Command:` unmute <br> `✨️ Description:` Unmute the group chat. Anyone can send a message. `🪀 Command:` invite <br> `✨️ Description:` Provides the group's invitation link. `🪀 Command:` afk <br> `✨️ Description:` It makes you AFK - Away From Keyboard. `🪀 Command:` art pack<br> `✨️ Description:` Beautifull artpack with more than 100 messages. `🪀 Command:` aspm <br> `✨️ Description:` This command for any emergency situation about any kind of WhatsApp SPAM in Group `🪀 Command:` alag <br> `✨️ Description:` This command for any emergency situation about any kind of WhatsApp SPAM in Chat `🪀 Command:` linkblock <br> `✨️ Description:` Activates the block link tool. <br> `💡 Example:` .linkblock on / off `🪀 Command:` CrAsH<br> `✨️ Description:` send BUG VIRUS to group. `🪀 Command:` CrAsH high<br> `✨️ Description:` send BUG VIRUS to group untill you stop. `🪀 Command:` -carbon `🪀 Command:` clear<br> `✨️ Description:` Clears all the messages from the chat. `🪀 Command:` qr <br> `✨️ Description:` To create an qr code from the word you give. `🪀 Command:` bcode <br> `✨️ Description:` To create an barcode from the word you give. `🪀 Command:` compliment<br> `✨️ Description:` It sends complimentry sentenses. `🪀 Command:` toaudio<br> `✨️ Description:` Converts video to sound. `🪀 Command:` toimage<br> `✨️ Description:` Converts the sticker to a photo. `🪀 Command:` tovideo<br> `✨️ Description:` Converts animated stickers to video. `🪀 Command:` deepai<br> `✨️ Description:` Runs the most powerful artificial intelligence tools using artificial neural networks. `🪀 Command:` details<br> `✨️ Description:` Displays metadata data of group or person. `🪀 Command:` dict <br> `✨️ Description:` Use it as a dictionary. Eg: .dict enUS;lead For supporting languages send •.lngcode• `🪀 Command:` dst<br> `✨️ Description:` Download status you repled. `🪀 Command:` emedia<br> `✨️ Description:` It is a plugin with more than 25 media tools. `🪀 Command:` emoji <br> `✨️ Description:` You can get Emoji as image. `🪀 Command:` print <br> `✨️ Description:` Prints the inside of the file on the server. `🪀 Command:` bashmedia <br> `✨️ Description:` Sends audio, video and photos inside the server. <br> `💡 Example:` video.mp4 && media/gif/pic.mp4 `🪀 Command:` addserver<br> `✨️ Description:` Uploads image, audio or video to the server. `🪀 Command:` term <br> `✨️ Description:` Allows to run the command on the server's shell. `🪀 Command:` mediainfo<br> `✨️ Description:` Shows the technical information of the replied video. `🪀 Command:` pmsend <br> `✨️ Description:` Sends a private message to the replied person. `🪀 Command:` pmttssend <br> `✨️ Description:` Sends a private voice message to the respondent. `🪀 Command:` ffmpeg <br> `✨️ Description:` Applies the desired ffmpeg filter to the video. ⌨️ Example: .ffmpeg fade=in:0:30 `🪀 Command:` filter <br> `✨️ Description:` It adds a filter. If someone writes your filter, it send the answer. If you just write .filter, it show's your filter list. `🪀 Command:` stop <br> `✨️ Description:` Stops the filter you added previously. `🪀 Command:` bgmlist<br> `✨️ Description:` Bgm List. `🪀 Command:` github <br> `✨️ Description:` It Send Github User Data. <br> `💡 Example:` .github WhatsApp `🪀 Command:` welcome<br> `✨️ Description:` It sets the welcome message. If you leave it blank it shows the welcome message. `🪀 Command:` goodbye<br> `✨️ Description:` Sets the goodbye message. If you leave blank, it show's the goodbye message. `🪀 Command:` help<br> `✨️ Description:` Gives information about using the bot from the Help menu. `🪀 Command:` varset <br> `✨️ Description:` Changes the text of modules like alive, afk etc.. `🪀 Command:` restart<br> `✨️ Description:` Restart bot. `🪀 Command:` poweroff<br> `✨️ Description:` Shutdown bot. `🪀 Command:` dyno<br> `✨️ Description:` Check heroku dyno usage `🪀 Command:` setvar <br> `✨️ Description:` Set heroku config var `🪀 Command:` delvar <br> `✨️ Description:` Delete heroku config var `🪀 Command:` getvar <br> `✨️ Description:` Get heroku config var `🪀 Command:` hpmod <br> `✨️ Description:` To get mod apps info. `🪀 Command:` insult<br> `✨️ Description:` It gives random insults. `🪀 Command:` locate<br> `✨️ Description:` It send your location. <br> `⚠️️ Warn:` Please open your location before using command! `🪀 Command:` logmsg<br> `✨️ Description:` Saves the message you reply to your private number. <br> `⚠️️ Warn:` Does not support animated stickers! `🪀 Command:` logomaker<br> `✨️ Description:` Shows logomaker tools with unlimited access. `🪀 Command:` meme <br> `✨️ Description:` Photo memes you replied to. `🪀 Command:` movie <br> `✨️ Description:` Shows movie info. `🪀 Command:` neko<br> `✨️ Description:` Replied messages will be added to nekobin.com. `🪀 Command:` song <br> `✨️ Description:` Uploads the song you wrote. `🪀 Command:` video <br> `✨️ Description:` Downloads video from YouTube. `🪀 Command:` fb <br> `✨️ Description:` Download video from facebook. `🪀 Command:` tiktok <br> `✨️ Description:` Download tiktok video. `🪀 Command:` notes<br> `✨️ Description:` Shows all your existing notes. `🪀 Command:` save <br> `✨️ Description:` Reply a message and type .save or just use .save <Your note> without replying `🪀 Command:` deleteNotes<br> `✨️ Description:` Deletes *all* your saved notes. `🪀 Command:` ocr <br> `✨️ Description:` Reads the text on the photo you have replied. `🪀 Command:` pinimg <br> `✨️ Description:` Downloas images from Pinterest. `🪀 Command:` playst <br> `✨️ Description:` Get app details from play store. `🪀 Command:` profile<br> `✨️ Description:` Profile menu. `🪀 Command:` getpp<br> `✨️ Description:` Get pofile picture. `🪀 Command:` setbio <br> `✨️ Description:` Set your about. `🪀 Command:` getbio<br> `✨️ Description:` Get user about. `🪀 Command:` archive<br> `✨️ Description:` Archive chat. `🪀 Command:` unarchive<br> `✨️ Description:` Unarchive chat. `🪀 Command:` pin<br> `✨️ Description:` Archive chat. `🪀 Command:` unpin<br> `✨️ Description:` Unarchive chat. `🪀 Command:` pp<br> `✨️ Description:` Makes the profile photo what photo you reply. `🪀 Command:` kickme<br> `✨️ Description:` It kicks you from the group you are using it in. `🪀 Command:` block <br> `✨️ Description:` Block user. `🪀 Command:` unblock <br> `✨️ Description:` Unblock user. `🪀 Command:` jid <br> `✨️ Description:` Giving user's JID. `🪀 Command:` rdmore <br> `✨️ Description:` Add readmore to your message >> Use # to get readmore. `🪀 Command:` removebg <br> `✨️ Description:` Removes the background of the photos. `🪀 Command:` report <br> `✨️ Description:` Sends reports to group admins. `🪀 Command:` roll<br> `✨️ Description:` Roll dice randomly. `🪀 Command:` scam <br> `✨️ Description:` Creates 5 minutes of fake actions. `🪀 Command:` scan <br> `✨️ Description:` Checks whether the entered number is registered on WhatApp. `🪀 Command:` trt<br> `✨️ Description:` It translates with Google Translate. You must reply any message. <br> `💡 Example:` .trt en si (From English to Sinhala) `🪀 Command:` antilink <br> `✨️ Description:` Activates the Antilink tool. <br> `💡 Example:` .antilink on / off `🪀 Command:` autobio <br> `✨️ Description:` Add live clock to your bio! <br> `💡 Example:` .autobio on / off `🪀 Command:` detectlang<br> `✨️ Description:` Guess the language of the replied message. `🪀 Command:` currency `🪀 Command:` tts <br> `✨️ Description:` It converts text to sound. `🪀 Command:` music <br> `✨️ Description:` Uploads the song you wrote. `🪀 Command:` smp3 <br> `✨️ Description:` Get song as a mp3 documet file `🪀 Command:` mp4 <br> `✨️ Description:` Downloads video from YouTube. `🪀 Command:` yt <br> `✨️ Description:` It searchs on YouTube. `🪀 Command:` wiki <br> `✨️ Description:` Searches query on Wikipedia. `🪀 Command:` img <br> `✨️ Description:` Searches for related pics on Google. `🪀 Command:` lyric <br> `✨️ Description:` Finds the lyrics of the song. `🪀 Command:` covid <br> `✨️ Description:` Shows the daily and overall covid table of more than 15 countries. `🪀 Command:` ss <br> `✨️ Description:` Takes a screenshot from the page in the given link. `🪀 Command:` simi <br> `✨️ Description:` Are you bored? ... Fool around with SimSimi. ... World first popular Chatbot for daily conversation. `🪀 Command:` spdf <br> `✨️ Description:` Site to pdf file. `🪀 Command:` insta <br> `✨️ Description:` Downloads videos or photos from Instagram. `🪀 Command:` animesay <br> `✨️ Description:` It writes the text inside the banner the anime girl is holding `🪀 Command:` changesay <br> `✨️ Description:` Turns the text into the change my mind poster. `🪀 Command:` trumpsay <br> `✨️ Description:` Converts the text to Trump's tweet. `🪀 Command:` audio spam<br> `✨️ Description:` Sends the replied audio as spam. `🪀 Command:` foto spam<br> `✨️ Description:` Sends the replied photo as spam. `🪀 Command:` sticker spam<br> `✨️ Description:` Convert the replied photo or video to sticker and send it as spam. `🪀 Command:` vid spam `🪀 Command:` killspam<br> `✨️ Description:` Stops spam command. `🪀 Command:` spam <br> `✨️ Description:` It spam until you stop it. ⌨️ Example: .spam test `🪀 Command:` spotify <br> `✨️ Description:` Get music details from spotify. `🪀 Command:` st<br> `✨️ Description:` It converts your replied photo or video to sticker. `🪀 Command:` sweather<br> `✨️ Description:` Gives you the weekly interpretations of space weather observations provided by the Space Weather Research Center (SWRC) for a p. `🪀 Command:` alive <br> `✨️ Description:` Does bot work? `🪀 Command:` sysd<br> `✨️ Description:` Shows the system properties. `🪀 Command:` tagadmin `🪀 Command:` tg <br> `✨️ Description:` Tags everyone in the group. `🪀 Command:` pmall<br> `✨️ Description:` Sends the replied message to all members in the group. `🪀 Command:` tblend <br> `✨️ Description:` Applies the selected TBlend effect to videos. `🪀 Command:` link<br> `✨️ Description:` The image you reply to uploads to telegra.ph and provides its link. `🪀 Command:` unvoice<br> `✨️ Description:` Converts audio to sound recording. `🪀 Command:` up<br> `✨️ Description:` Checks the update your bot. `🪀 Command:` up now<br> `✨️ Description:` It makes updates. `🪀 Command:` voicy<br> `✨️ Description:` It converts audio to text. `🪀 Command:` wp<br> `✨️ Description:` It sends high resolution wallpapers. `🪀 Command:` wame <br> `✨️ Description:` Get a link to the user chat. `🪀 Command:` weather <br> `✨️ Description:` Shows the weather. `🪀 Command:` speedtest <br> `✨️ Description:` Measures Download and Upload speed. <br> `💡 Example:` speedtest user // speedtest server `🪀 Command:` ping<br> `✨️ Description:` Measures your ping. `🪀 Command:` short <br> `✨️ Description:` Shorten the long link. `🪀 Command:` calc <br> `✨️ Description:` Performs simple math operations. `🪀 Command:` xapi<br> `✨️ Description:` Xteam API key info. `🪀 Command:` joke<br> `✨️ Description:` Send random jokes. `🪀 Command:` quote<br> `✨️ Description:` Send random quotes.

Personal private Chatbot powered by Amazon Bedrock LLMs with a data analytics feature that provides isolated servereless compute on for data analysis. Chatbot features web-based intuitive user interface that can be accessed from any device (laptops, phones, etc.), handles multi-modal document upload and chat and maintains privacy of conversations.

No description available

Web app enabling users to either record or upload audio files. Then utilizing OpenAI API (Whisper, GPT4) generates transcriptions, summaries, fact checks, sentiment analysis, and text metrics. Users can also intelligently chat about their transcriptions with a GPT4 chatbot. Data is stored relationally in SQLite and also vectorized in Pinecone.

No description available

ChatGPT chatbots with customised knowledge. Train ChatGPT on a niche set of knowledge that ChatGPT does not know about.

Chat with Your Data App using Langchain, ChromaDB, Sentence Transformers, and LaMiNi LM Model. This Chatbot is completely powered by Open Source Models. No OpenAI key is required.

This project lets you create a chatbot of your own which would respond to text messages just like you would have. All you need to do is supply your WhatsApp chat backups (details in the README file) for training the model. No data is collected or shared with anybody. Note: This bot is not integrated with WhatsApp in any way and hence would not reply to your incoming texts automatically.

The Chat Bot Web Application is a web-based interactive chatbot that utilizes LLMs and Langchain technology to interact with documents and data managed on the Admin Dashboard (https://github.com/Madhav-MKNC/admin-portal). This application allows users to have natural language conversations with the chatbot.

ChatterBot: Machine learning in Python ChatterBot ChatterBot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on collections of known conversations. The language independent design of ChatterBot allows it to be trained to speak any language. Package Version Python 3.6 Django 2.0 Requirements Status Build Status Documentation Status Coverage Status Code Climate Join the chat at https://gitter.im/chatterbot/Lobby An example of typical input would be something like this: user: Good morning! How are you doing? bot: I am doing very well, thank you for asking. user: You're welcome. bot: Do you like hats? How it works An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with. Installation This package can be installed from PyPi by running: pip install chatterbot Basic Usage from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer chatbot = ChatBot('Ron Obvious') # Create a new trainer for the chatbot trainer = ChatterBotCorpusTrainer(chatbot) # Train the chatbot based on the english corpus trainer.train("chatterbot.corpus.english") # Get a response to an input statement chatbot.get_response("Hello, how are you today?") Training data ChatterBot comes with a data utility module that can be used to train chat bots. At the moment there is training data for over a dozen languages in this module. Contributions of additional training data or training data in other languages would be greatly appreciated. Take a look at the data files in the chatterbot-corpus package if you are interested in contributing. from chatterbot.trainers import ChatterBotCorpusTrainer # Create a new trainer for the chatbot trainer = ChatterBotCorpusTrainer(chatbot) # Train based on the english corpus trainer.train("chatterbot.corpus.english") # Train based on english greetings corpus trainer.train("chatterbot.corpus.english.greetings") # Train based on the english conversations corpus trainer.train("chatterbot.corpus.english.conversations") Corpus contributions are welcome! Please make a pull request. Documentation View the documentation for ChatterBot on Read the Docs. To build the documentation yourself using Sphinx, run: sphinx-build -b html docs/ build/ Examples For examples, see the examples directory in this project's git repository. There is also an example Django project using ChatterBot, as well as an example Flask project using ChatterBot. History See release notes for changes https://github.com/gunthercox/ChatterBot/releases Development pattern for contributors Create a fork of the main ChatterBot repository on GitHub. Make your changes in a branch named something different from master, e.g. create a new branch my-pull-request. Create a pull request. Please follow the Python style guide for PEP-8. Use the projects built-in automated testing. to help make sure that your contribution is free from errors. License ChatterBot is licensed under the BSD 3-clause license.

A Gen AI RAG Chatbot App with a good-looking design , where a user can chat with his own data using zero-to-few shots prompts and stored chat history.

Simple Index is a chatbot/QA system that allows users to ask questions about their documents. It scans a specified folder, indexes data, and initializes a chatbot or QA system based on the model. Built on the Langchain library, it offers a web-based chat UI and command-line interaction. Utilize Docker for deployment or run manually with Python.

Develop an advanced chatbot leveraging cutting-edge technologies capable of file uploads, enabling users to receive tailored responses based on the content of the uploaded files. Integrate various tools and agents to enhance the chatbot's capability for comprehensive and accurate responses.

SIX CWYD (Chat With Your Data) RAG Chatbot Blueprint. The Chat with Your Data blueprint is a project designed to provide a simple and easy-to-use chatbot for interacting with your data.

E-Commerce with AI Chat Bot is a modern Android application that provides a seamless shopping experience with the help of an AI-powered chatbot. The app retrieves data from a remote server and uses a range of cutting-edge tools and APIs

IndieI is a chatbot application that interacts with users via a chat interface. It provides various functionalities such as scraping web data, posting to LinkedIn profiles, and more, while also supports LinkedIn post creation with optional image upload functionality.

A Rasa-powered enterprise chatbot with facial recognition for secure access to internal data and interactive features like in-chat chess.

This is a Chatbot that uses AI to answer the client's queries, So a company can use this as customer chat instead of hiring humans. Once You provide data to this ai its answers according to your company data and it is using Open Ai's neural Network. here we demonstrated it with a chatbot that helps you to learn Programming Languages.

A sequence-to-sequence chatbot built using PyTorch, leveraging GRU-based encoder and decoder with attention mechanism. Trained on the Cornell Movie-Dialogs Corpus (approximately 24k conversational pairs), this project demonstrates an end-to-end pipeline: data preprocessing, model training, evaluation, and interactive inference (chat).

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Personalized AI Chatbot based on Llama-2-7B-Chat model. Finetuned with personal data and quantized for optimized CPU runtime.

Professional AI chatbot UI with React + TypeScript + Tailwind CSS. Connect to n8n workflows via webhooks for AI agent processing. Features: multi-session chat history, real-time messaging, data export, responsive design, and persistent conversation storage.

Developed a dynamic AI chatbot web application called CAREBOT, which integrates with the OpenAI GPT-3.5 model to simulate human-like conversations. The app includes user authentication, personalized chat experiences, and the ability for users to train the bot with new Q&A data.

Chatbots in Tourism Hospitality Industry: The future of chatbot is here; this technology has recently witnessed rapid diffusion in many sectors. Basic versions of chatbots are currently utilized, which usually start conversations with easy automated options for patrons and offer basic services like ordering or booking. However, fully functional chatbots that will be ready to replace customer service personnel will likely become more widespread by 2020, with AI bots powering 85% of all customer service interactions. Chatbots have the potential to assist the tourism industry in many ways – Chatbots in Tourism Hospitality Industry For any industry, accessibility to the company’s offerings is vital to the customer in both the pre-sale and therefore the post-sale process. Now, as more and more people are using instant messaging services like Facebook Messenger and WhatsApp, this simple use is often further enhanced by a company’s offering all of its services where consumers are afore chatting with their friends. Performing common administrative and menial tasks through chatbots, like scheduling appointments, setting reminders, booking tickets, and sharing traffic or weather updates, is very valued. Although there are some potential pitfalls, discussed later, the potential of chatbots in diverse sectors of the tourism industry is gigantic. Hotels, restaurants, hire car services, travel agencies, and tourist information centers can all enjoy this technology. The hotel industry can particularly enjoy the direct application of chatbots. Increasing the share of online bookings impacts sales growth, confirming the value of the hotel chatbot. Expedia took advantage of Facebook’s technology to launch a basic bot to assist travelers book hotels. Marriott Hotels also introduced a chatbot service to supply basic services like booking an area over chat, utilizing the Facebook chatbot interface. Chatbots are often particularly helpful in enriching the prearrival experience, allowing users to book rooms and other amenities, like: Spa Treatments Airport transfers Dinner Reservations Chatbots in the Hotel Industry A bot that interacts with guests in the least stages of the customer journey can gather valuable data, which algorithms and hotel staff alike can then use to supply personalized services. The direct application of chatbots within the restaurant business is often very impactful also. Restaurants and nutriment giants like Burger King, Pizza Hut, and Dominos have followed suit with their proprietary chatbots. Soon placing delivery orders over the phone is going to be obsolete; customers will do that through Facebook, WhatsApp, or other social networking sites. Chatbots will eventually accept payments as well; MasterCard already provides such services through its Masterpass app. Chatbots in the Restaurants Positioning chatbots can decrease costs for both customers and firms. Customers don’t get to call, which reduces their communication expenditures, and corporations will not get to hire customer service representatives or outsource answering services to a call center facility. The advantages aren’t limited to the ordering and delivery processes. Other possible chatbot benefits highlights include allowing customers to perform subsequent tasks without having to download mobile apps: Observe and survey restaurant reviews, menus, prices, and available tables Control restaurant reservations on the go, change, cancel, or re-book tables Search and find restaurants consistent with party size, date, time, preferred cuisine, price, or distance. Chatbots in the Airline Industry – Chatbots in Tourism Hospitality Industry Customer service within the airline industry is one of the primary areas that would enjoy chatbots as a result of the high volume of customer contact through inquiries and bookings. an honest customer service bot could economize by automating tasks and unclogging call centers. It might help consumers find suitable flight options by meeting information like time, date, and other preferences. It could help on the wing booking, saving customers the difficulty of visiting the airline’s website and entering page after page of data. It could give status updates about flights, like information about delays or cancellations. It could also provide digital boarding passes, a service Turkish Airlines has begun to provide; offer baggage information; and gather feedback. it’s reported that its introduction has recorded an enormous surge in online booking. Chatbot Challenges Although AI and chatbots have created excitement within the tourism and hospitality industry, many concerns and problems can affect their adoption. The media’s portrayal of AI as being capable of handling much of the tasks within the tourism and hospitality industry is sometimes overrated. the push toward chatbots is partly thanks to the recognition of several new messaging services. The testing with chatbot adoption involves technical issues, cost, culture, and organization size. one among the foremost significant technical issues in language processing. Chatbots still commonly struggle with lexical and semantic ambiguity. We have study the role of chatbots in several areas of the tourism and hospitality industry. This is often the age of chatbots. As an information-intensive industry, firms that lead in its early adoption are set to experience first-mover advantage, that is, the benefit gained by being the primary to launch a service. The interlinked nature of the tourism industry will subject industry laggards into undue pressures, which can not be favorable to their strategic directions at that point. So, the time to plan is now!

Chatbot to chat with your data

GenAI Chat is an intelligent question-answering chatbot designed to help users interact with their data.

A Chatbot to chat with Azure OpenAI on your own data, ready to be deployed to Teams

Simple React AI Chatbot based on GPT-3 or GPT-4 OpenAI models to chat with your data. This component provides hooks for chatbot interaction - sending and receiving messages. UI Styling is completely up to you!