Vasishta

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:

GL_ML_Deployment

HackingNeuralNetworks

A small course on exploiting and defending neural networks

Vasishta/awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

Vasishta/aws-workshop

Learn to deploy real applications in a scalable way, using Amazon Web Services.

Vasishta/cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

Vasishta/Deep-Trading

Algorithmic trading with deep learning experiments

Vasishta/DeepLayout

Deep learning based page layout analysis

Vasishta/devtraining-needit-tokyo

This repository is used by the Developer Site training content, Tokyo release.

Vasishta/github-slideshow

A robot powered training repository :robot:

Vasishta/GL_ML_Deployment

Vasishta/HackingNeuralNetworks

A small course on exploiting and defending neural networks

Vasishta/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.

Vasishta/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.

Vasishta/llm-foundation-models

Vasishta/MEDIUM_NoteBook

Repository containing notebooks of my posts on Medium

Vasishta/mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Vasishta/nlp_pipe_manager

A pipeline for NLP projects using SkLearn

Vasishta/PythonFlask-JobBoard

Build a Job Board with Python & Flask

Vasishta/SNABook

Code for "Social Networks for Startups"

Vasishta/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.

Vasishta/toolkit

My Toolkit for Machine Learning and Data Science.

Vasishta/Udacity_DeepLearning

DLND

Vasishta/validation

Overview of validation techniques

Vasishta/wtfpython

A collection of surprising Python snippets and lesser-known features.

Vasishta/YelpDatasetChallengeDataScienceAndMachineLearningUCSD