sololearner9's repositories
100DaysOfMLCode
This repository contains all the required resources regarding the 100+ Days Of ML Code Telgram Group which was driven by me from 1-1-2019 to 31-12-2019 !!
awesome-computer-vision
A curated list of awesome computer vision resources
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-github-profile-readme
😎 A curated list of awesome Github Profile READMEs 📝
basics
📚 Learn ML with clean code, simplified math and illustrative visuals. As you learn, work on interesting projects and share them on https://madewithml.com for the community to discover and learn from!
coursera-deep-learning
Solutions to all quiz and all the programming assignments!!!
cs229-ps-2018
My solutions to the problem sets of Stanford cs229, 2018
CSpuare_FaceRecognition
Face_Recognition project, meant to be used for attendance system in IIT hostels under CSquare program, IITPKD
Data-science-best-resources
Carefully curated resource links for data science in one place
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Deep-learning-with-Python
Deep learning codes and projects using Python
DeepDL
Algorithms in Python ~ Deep Learning
effective-pandas
Source code for my collection of articles on using pandas.
GenZoo
A repository providing implementations of generative models in various frameworks.
IIT-JEE-Chemistry-Books
"Chemistry plays a vital role in our understanding of life, the universe and the chances of a better future." - Michelle Francl
learnopencv
Learn OpenCV : C++ and Python Examples
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
neuroscience-ai-reading-course
Notes for the Neuroscience & AI Reading Course (SEM-I 2020-21) at BITS Pilani Goa Campus
NVAE
The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder"
pandas_exercises
Practice your pandas skills!
Papers-Literature-ML-DL-RL-AI
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
Research-Internships-for-Undergraduates
List of Research Internships for Undergraduate Students
SelfDrive
⛊ Self Driving Car with Deep Learning
stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
The-Mechanics-of-Machine-Learning
The Mechanics of Machine Learning Book contents Work in progress Book version 0.4 Terence Parr and Jeremy Howard Copyright © 2018-2019 Terence Parr. All rights reserved. Please don't replicate on web or redistribute in any way. This book generated from markup+markdown+python+latex source with Bookish. You can make comments or annotate this page by going to the annotated version of this page. You'll see existing annotated bits highlighted in yellow. They are PUBLICLY VISIBLE. Or, you can send comments, suggestions, or fixes directly to Terence. Warning: The content of this book is so unexciting that you'll be able to use it in your actual job! This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. We focus on just a few powerful models (algorithms) that are extremely effective on real problems, rather than presenting a broad survey of machine learning algorithms as many books do. Co-author Jeremy used these few models to become the #1 competitor for two consecutive years at Kaggle.com. This narrow approach leaves lots of room to cover the models, training, and testing in detail, with intuitive descriptions and full code implementations. This is a book in progress and we will add chapters and make edits
Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
Tools to Design or Visualize Architecture of Neural Network
VMLS-Companions
These are companion notebooks written in Julia and Python for: "Introduction to Applied Linear Algebra" by Boyd and Vandenberghe.