- Stanford CS229 Linear Algebra Review
- The Matrix Cookbook
- Linear algebra cheat sheet for deep learning - Towards DataScience
- Probability Cheatsheet v2.0
- 109 Data Science Interview Questions and Answers
- 40 Interview Questions On Statistics For Data Scientists
- Prob and Statistics CookBook
- AI Accelerators, Adi Fuchs on Medium
- Setting Up Deep Learning Machines
- Choosing GPUs for Deep Learning important
- ImageNet
- COCO
- MNIST
- Fashion MNIST
- CalTech-101
- CIFAR-10
- Hugging Face
- Dataset Search
- Open Registry AWS
- Data Gov
- Open Data Gov Canada
- Kaggle Datasets
- Quick Draw with Google
- CelebA Dataset
- Awesome Public Datasets
- Laion AI
- YouTube 8M
- Masader
- HumanEval - OpenAI LLMs
- Academic Torrent
- Sqlbot interactive sql tutorials
- Advanced Programming in the UNIX Environment
- Advanced Programming in Unix Env book & code
- GNU official webpage all gnu software and docs
- Linux.org
- The Art of Command Line
- Tmate pair programming | share terminal
- POSIX
- How to Become a Unix Wizard
- How I Learned Linux
- The Unix and Internet Fundamentals HOWTO
- How Docker Can Help you become an effectice DataScientist - Hamel Hussain
- Rootless Containers from Scratch, Liz Rice
- Docker Notes, Hamel Hussain
- Docker Notes from Lavanya.ai
- Docker Cheatsheet
- Nvidia Docker Build and run Docker containers leveraging NVIDIA GPUs
- Pro Git book
- Version Control, MIT
- Oh Shit Git! useful commands
- Cheatsheet
- Scheme.org
- The Scheme Programming Language, by R. Kent Dybvig
- Revised Report on the Algorithmic Language Scheme
- PyTorch GANs
- Keras GANs
- NVIDIA's Deep Imagination Team's PyTorch Library
- DeepFakes, FaceSwap
- DeepFace Lab
- StudioGAN
- Neural Style Transfer TensorFlow Python API implementation
- Neural Style Transfer Keras implementation of paper
- Fast Neural Style
- Neural Style Transfer Torch implementation of paper
- AnimeGAN2 PyTorch implementation of AnimeGAN2
- AnimeGAN2 TensorFlow implementation of AnimeGAN2
- StyleGAN official TensorFlow implementation
- StyleGAN2 PyTorch implementation
- StyleGAN2-ADA
- CycleGAN and Pix2pix
- GFP-GAN
- Spinning Up in Deep RL
- Reinforcement Learning, An Introduction github
- Implementations of RL Algorithms github
- Deep RL Hands On github
- Minimal and Clean RL Examples github
- Deep Reinforcement Learning for Keras github
- MinimalRL github
- CleanRL github
- DeepLearning Flappy Bird github
- DeepRL Hacks, William Falcom
- Stable-Baselines3 Docs, Reliable RL Implementations
- The Unity Machine Learning Agents Toolkit
- Interview Qs for a CV Engineer?
- CNN Cheatsheet
- Reproducing Yann LeCun 1989 paper, Andrej Karpathy
- YOLOv3
- YOLOv4
- YOLOv5
- YOLOv6
- MaskRCNN
- Mask R-CNN
- ImageAI
- GluonCV
- PyTorch Image Models
- Object Detection Models - TF
- Detectron - FAIR
- Detectron2 - FAIR
- Minimal YOLOv3 PyTorch
- DenseNet
- Deep Daze
- Vit PyTorch
- KerasCV
- MobileNet SSD Caffe implementation
- DenseNet
- U-Net
- UNet Implementation
- Building a RecSys using Deep Learning, Abhishek Thakur youtube
- Best practices on RecSys, Microsoft
- RecSys at Google
- Nvidia Merlin
- DL Recc Model - Facebook
- Implementation of DLRM, Facebook
- Robotis Course from UMichigan
- Aerial Robotics, Vijay Kumar
- Control of Mobile Robots, Georgia Tech
- Modern Robotics, Coursera
- OpenPilot, CommaAI
- Point cloud diffusion for 3D model synthesis, OpenAI
- CoppeliaSim
- Mujoco, DeepMind
- Stable Diffusion
- Stable Diffusion 2
- DALLE text-to-image
- CLIP
- PyTorch Geometric github
- GraphSAGE github
- DeepWalk, DL for Graphs github
- Graphein Protein Graph Library
- AutoSKLearn Automated Machine Learning with scikit-learn
- AutoKeras AutoML library for deep learning
- Neural Network Intelligence An open source AutoML toolkit for automate machine learning lifecycle
- The Incredible PyTorch
- Annotated PyTorch Paper Implementations
- Deep Learning with PyTorch youtube
- Distributed PyTorch, Mark Saroufim
- Tensor Puzzles, Code from Scratch
- Materials to Learn PyTorch
- The Most Complete Guide to PyTorch for Data Scientists
- PyTorch Cheatsheet
- Imagen PyTorch
- PyTorch Tutorials official docs
- A PyTorch Tools, best practices & Styleguide github
- Automatic Differentiation, Mark Saroufim
- PyTorch Models with Jetson Nano
- Simple tutorials using Google's TensorFlow Framework
- Awesome TensorFlow
- TensorFlow API
- A WebGL accelerated JavaScript library for training and deploying ML models.
- FastAI DeepLearning Library source code
- Deep Learning with Python, Francois Chollet notebooks
- DeepLearning for Coders with Fastai and PyTorch
- Hands-on Machine Learning with Scikit-Learn and TensorFlow
- AI Notebooks, Geohot
- Data Science Python Notebooks
- ML Notebooks, Dair
- DL Colab Notebooks
- DeepLearning Fundamentals, Colab
- Training a Neural Network? Start Here! - Lavanya W&B
- A Recipe for Training Neural Networks, Andrej Karpathy
- Neural Networks:Zero to Hero, Andrej Karpathy Andrej Karpathy
- ML from Scratch
- Deep Learning Tuning Playbook, Google Research
- Machine Learning Mastery
- Awesome Machine Learning
- Probabilistic Machine Learning
- ML Algortihms Implementations
- Machine Learning Complete
- 100+ ML Algorithms & Models Explained with Python
- Machine Learning Tutorials github
- Model Zoo model code collection
- Awesome DataScience github repo to learn and apply DS
- Python Code for ML, NLP, DL, and RL Python codes in Machine Learning, NLP, Deep Learning
- Machine Learning Revision
- Super Machine Learning Revision Notes
- Stanford CS229 ML Cheatsheets - Tips & Tricks
- Build a career in datascience book
- AI Notes from DeepLearning.ai
- Lavanya's Note's on Machine Learning
- Cheatsheet of ML and Python - Medium Blog
- Cheatsheets for Data Science and Machine Learning - Google Sites
- MLE 200+ Flashcards
- Awesome DataScience Cheatsheet - Kaggle
- Software 2.0, Andrej Karpathy
- DistillPub
- ConvNetJS, DeepLearning in your Browser
- An Overview of Gradient Descent Optimization Algorithms, Sebastian Ruder
- Colah's Blog
- Image Kernels, Explained Visually
- Making Deep Learning Go Brrrr from First Principles
- An Overview of Gradient Descent Optimizaiton Algorithms - Sebastian Ruder
- Exploring Hyperparameter Meta-Loss Landscapes with JAX
- Parallelizing NNs on 1 GPU with JAX
- Gradient Descent: All You Need to Know
- The Vanishing Gradient Problem - Towards DataScience
- Train with Mixed Precision - Nvidia DeepLearning
- Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020
- Andrej Karpathy Fave Tweets
- Loss Functions, Adnrej Karpathy
- Yes you should understand backprop, Andrej Karpathy
- What is Softmax Regression, Sebastian Raschka
- Giving GPT-3 a Turing Test
- Too much efficiency makes everything worse: overfitting and the strong version of Goodhart's law
- Reverse Engineering Copilot
- Different Paths to AGI, John Carmack
- 100 Page ML Book
- Ace the Data Science Interview
- Deep Learning book from Ian Goodfellow, et al
- Deep Learning with Python, Francois Chollet
- Hands-on Machine Learning with Scikit-Learn and TensorFlow
- DeepLearning for Coders with Fastai and PyTorch
- Natural Language Processing with Python
- Deep Learning in Production, Sergios Karagiannakos code repo
- Neural Networks and Deep Learning
- Dive into Deep Learning code repo
- Machine Learning with PyTorch and Scikit-Learn code repository
- Python DataScience HandBook code repo
- Python for Data Analysis code repo
- AI and Machine Learning for Coders, Laurence Moroney
- Modern DeepLearning for Tabular Data, Ye & Wang
- Modern Time Series Forecasting with Python, Joseph
- The Kaggle Book
- NLP with Transformers
- DeepLearning AI
- Fast AI
- Elements of Statistical Learning
- Neural Networks and DeepLearning
- CS231n: Deep Learning for Computer Vision
- http://web.stanford.edu/class/cs224n/
- CS224W: Machine Learning with Graphs
- CS 285 at UC Berkeley Deep Reinforcement Learning
- Applied Machine Learning - Cornell Tech
- Logikbot
- DeepLearning, New York University taught by YanLeCun and Alfredo Canziani
- Applications of DNNs, Washington University
- Data Preparation and Feature Engineering in ML, Google
- Prompt Engineering Guide, DAIR
- Grokking Deep Learning, Andrew Task
- Machine Learning Tutorials, Github
- Machine Learning Interviews Book - Chip Huyen
- FastAI Wiki: ML Interviews Resources & Advices
- Common ML Interview Q's - Quora
- Top 100 ML Q&A's - TechTutorial
- 200+ ML Q&A's 2021 - mygreatlearning
- Mathematics for ML, DL, and general ML Questions - Github
- Data Science Q&A's Interviews
- Data Science Q&A's - Github
- Cracking the ML Interview Github Q&A - Github
- Crack the top 40 ML Interview Q&A's - Educative.io
- ML Interviews Q's - Github
- 51 Essential ML Q&A's - SpringBoard
- Commonly used Machine Learning Algorithms (with Python and R Codes)
- Interview Prep: 40 AI Q&A's- SpringBoard
- ML GeekesforGeeks Algorithms & Code
- Machine Learning Interview Questions - InterviewBit
- Datascience Interview Preparation Resources