Ashish Gupta's repositories
Hierarchical-Attention-Networks-for-Document-Classification-NAACL-2016
This repository contains Keras implementation of Hierarchical Attention Networks for Document Classification(NAACL 2016)
Deep-Recurrent-Generative-Decoder-for-Abstractive-Text-Summarization-EMNLP-2017
Sequence to sequence oriented encoder decoder model with attention mechanism and variational auto encoders.
Toxic-Comment-Classification
Challenge is to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. Data comprises of comments from Wikipedia’s talk page edits.
Ashish-Gupta03.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
awesome-nlp
:book: A curated list of resources dedicated to Natural Language Processing (NLP)
awesome-rl
Reinforcement learning resources curated
Google-Interview-University
A complete daily plan for studying to become a Google software engineer :)
Hands-On-Meta-Learning-With-Python
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
keras-resources
Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library
markdown-cheatsheet
Markdown Cheatsheet for Github Readme.md
MLInterview
:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!
natural-language-processing
Resources for "Natural Language Processing" Coursera course.
q_learning_demo
This is the code for "How to use Q Learning in Video Games Easily" by Siraj Raval on Youtube
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
reinforcement-learning-an-introduction
Python implementation of Reinforcement Learning: An Introduction
Sequence-Labeling
This repo contains code using sequence tagging to predict the likely sequences via CRF as well as LSTM