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Kabir Ahuja's Projects

askme icon askme

An AI Chat bot from which you can ask about the important topics discussed in a business meeting and it answers your queries from its knowledge base of minutes of the meet. Different classification Algorithms were used to classify the questions asked to the chat-bot and then various NLP techniques like keyword searching, part of speech tagging and text summarization were used to answer the questions.

cancersurvivalprediction icon cancersurvivalprediction

Classifier to predict that a given patient will survive after one year on undergoing a particular cancer surgery.

datasets icon datasets

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

deepqlearning icon deepqlearning

Implementation of Deep Mind's Paper titled: "Human Level Control through Deep Reinforcement Learning"

ethics-reading-list icon ethics-reading-list

A list of ethics related resources for researchers and practitioners of Natural Language Processing and Computational Linguistics

facial_expression_recognition icon facial_expression_recognition

A program which uses Convolutional Neural Networks to classify facial expressions from an image into 7 categories: Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral. The whole code was implemented in python using TensorFlow. Different Techniques like Transfer Learning, Data Augmentation and Batch Normalisation were used to improve the performance.

hub icon hub

🔌 A central repository collecting pre-trained adapter modules

litmus icon litmus

AI Assistant for Building Reliable, High-performing and Fair Multilingual NLP Systems

matrad icon matrad

An open source multi-modality radiation treatment planning sytem

mega icon mega

Multilingual Evaluation of LLMs

mingpt icon mingpt

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

neuralnetsvisualizations icon neuralnetsvisualizations

An example of binary classification to demonstrate how neural nets are able to approximate highly complex functions accurately.

pyenv icon pyenv

Simple Python version management

questionansweringsquad icon questionansweringsquad

Implementing a question answering system using Stanford's Question Answering Dataset (SQUAD). Different Attention models like BIDAF, Coattention and Self Attention have been implemented yet. The code is implemented as the default project of the course CS224n.

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