WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- Browser Based PDF manager
- Resume Script Generator
- Physicc : A Simple Physics Engine
- Image Colorization
- Language Model Based Syntax Autocompletion in a Text Editor
- Computer vision based web app
- Cribbit Cribbit (Open for PGs Only)
- Techster Texter
- Language Detection
- Book Tracker
- ResoBin - Not the bin we deserve but the bin we need!
- Agree to disagree
- Watson (World's smartest assistant in your pocket)
- Meta Learning - Learning to Learn
- Break free of the matrix, by building one!
- Procedurally Generated Infinite Open World
- Introduction to App Development
- PAC MAN
- Introduction to Web Development
- Goal ICPC
- Traffic congestion modelling and rendering
- Tools for Data Science
- Machine Learning Based Metropolitan Air Pollution Estimation
- Audio controlled drone
- NLPlay with Transformers
- DIY FaceApp
- A Deep Dive into CNNs
- Competitive Coding
- Snake AI
- Facial Recognition App
- Gaming meets AI !!!
- R(ea)L Trader
- Computational Geometry
- Deep reinforcement learning - 2048 AI
- Reinforcement Learning to Finance
- Developing Hybrid ANN-Statistical Model for Robust Stock Market Prediction
- Astronomical Data-modelling and Interpretation
- Visual Perception for Self Driving Cars
- Convolutional Neural Networks and Applications
- Quantum Computing Algorithms
- Algorithm Visualizer
- Anime Club IITB Website using Django
- Machine Learning in Browser
A conversational chatbot is a software that conducts conversation via auditory or textual methods. This project aims to build a closed-domain, generative-based conversational chatbot from scratch.
The following will be executed :
Speech recognition that allows the device to capture words, phrases and sentences as the user speaks and convert to text. Speech synthesis that allows the device to speak. Usage of SpeechRecognizer library, Google web speech API, PyAudio package.
Natural language processing (NLP) for the device to understand the manually entered or converted text and find the best suitable reply. Machine learning algorithms such as RNNs, LSTM, and Sequence to Sequence model will be implemented from scratch in python. Datasets such as the Reddit conversation datasets, Cornell movie-dialogs corpus, or Twitter feed data can be used for training the NLP model. Exposure to PyTorch.
Build an android app using Android Studio as the conversational interface. The interface displays the entire conversation between the bot and the user.
Prior experience in Python coding. Basic android app development skills are desirable.
Points to be included in proposal :
Write your motivation and understanding of the project briefly. Background in ML/DL ( mention previous projects, if any ). Do specify it if you have no prior experience. Rough timeline explaining what you intend to learn and complete each week.
Tentative Project Timeline
|Week Number||Tasks to be Completed|
|Week 1||Read the theory related to speech recognition & synthesis. Brush up Python skills.|
|Week 2||Implement recording speech through microphone and conversion to text. Also, write the code for speaking the reply returned by the NLP algorithm.|
|Week 3||Get familiarised with the bag of words model & neural networks theory.|
|Week 4||Read the theory on RNNs, LSTM, sequence to sequence model thoroughly.|
|Week 5||Implement conversation flow & identification of intents, and entities.|
|Week 6||Train and implement the sequence to sequence model. Complete NLP code.|
|Week 7||Design the conversational interface on Android Studio. Start learning to write code for the functioning of the app.|
|Week 8||Implement code fully. Debugging.|