Vasishta's repositories
awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
aws-workshop
Learn to deploy real applications in a scalable way, using Amazon Web Services.
cheatsheets-ai
Essential Cheat Sheets for deep learning and machine learning researchers
Deep-Trading
Algorithmic trading with deep learning experiments
DeepLayout
Deep learning based page layout analysis
devtraining-needit-tokyo
This repository is used by the Developer Site training content, Tokyo release.
github-slideshow
A robot powered training repository :robot:
HackingNeuralNetworks
A small course on exploiting and defending neural networks
LikesOnGithub
This repository contains all the repositories I like on Github. This uses a Chrome Extension on https://github.com/Idnan/like-on-github. Would try and categorise them into related topics at a later stage.
Linear-Attention-Recurrent-Neural-Network
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN.
MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
mit-deep-learning
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
nlp_pipe_manager
A pipeline for NLP projects using SkLearn
PythonFlask-JobBoard
Build a Job Board with Python & Flask
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
validation
Overview of validation techniques