Mayank Hasija's starred repositories
100-Days-Of-ML-Code
100 Days of ML Coding
pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
DeepLearning.ai-Summary
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
PocketFlow
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
complete-web-developer-manual
All resources and notes from the Complete Web Developer in 2022: Zero to Mastery course
machine-learning-engineering-for-production-public
Public repo for DeepLearning.AI MLEP Specialization
Deep-Learning-Coursera
Deep Learning Specialization by Andrew Ng, deeplearning.ai.
deep-learning-illustrated
Deep Learning Illustrated (2020)
deeplearning.ai
deeplearning.ai , By Andrew Ng, All video link
100-days-of-code-python
100 Days of Code: The Complete Python Pro Bootcamp
Screeni-py
A Python-based stock screener to find stocks with potential breakout probability from NSE India.
Full-Stack-React-Projects-Second-Edition
Full-Stack React Projects - Second Edition, published by Packt
Deep-Learning-Specialization-Coursera
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
Coding-Ninjas-Full-Stack-Web-Development
It contains all the files I created during the MERN full stack web development course with coding ninjas
fastText-for-AI-Challenger-Sentiment-Analysis
AI Challenger 2018 Sentiment Analysis Baseline with fastText
fastai-course-1
Docker environment for fast.ai Deep Learning Course 1 at http://course.fast.ai
100-Days-of-Data-Science
100 Days of Data Science
fast.ai_notes
:notebook: Notes for fast.ai courses: intro to ML, Practical DL and Cutting edge DL.
address-matching
Python script for matching a list of messy addresses against a gazetteer using dedupe.
fastai-docker
Fast.AI course complete docker container for Paperspace and Gradient
Stanford-University-Statistical-Learning
Repo for Statistical Learning course offered by Stanford University
Introduction-to-Statistics
Introduction to Statistics Stanford University
Stanford-StatisticalLearning
Stanford Online course STATSX0001 "Statistical Learning" follows closely the sequence of chapters in the course text "An Introduction to Statistical Learning, with Applications in R" (James, Witten, Hastie, Tibshirani - Springer 2013). Trevor Hastie Professor of Statistics and of Biomedical Data Sciences, Stanford University, and Robert Tibshirani Professor of Biomedical Data Science and Statistics, Stanford University
100DaysOfMLCode
This repo is for the Siraj's 100 Days Of ML Code Challenge