Books

• Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron is the best selling book because it's great.
• https://github.com/fastai/fastbook The fastai book, published as Jupyter Notebooks
• https://www.deeplearningbook.org/ math heavy book by Ian Goodfellow and Yoshua Bengio and Aaron Courville
• https://christophm.github.io/interpretable-ml-book/ A Guide for Making Black Box Models Explainable
• https://themlbook.com/ The Hundred-Page Machine learning book by Andriy Burkov

Reinforcement Learning

Building a machine that senses the environment and then chooses the best policy (action) to do at any given state to maximize its expected long-term scalar reward is the goal of reinforcement learning.

• OpenAI Spinning Up This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
• Basic Reinforcement Learning An introduction series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.
• Advanced Topics: RL 2015 (COMPM050/COMPGI13) by David Silver (the guy behind AlphaGo)
• An Introduction Book by Richard S. Sutton and Andrew G. Barto
• Deep Reinforcement Learning: Pong from Pixels
• Lecture 10: Reinforcement Learning - YouTube
• A Survey Paper
• Deep Reinforcement Learning: A Tutorial - OpenAI
• CS 294: Deep Reinforcement Learning, Spring 2017

Deep Learning

Deep learning is a branch of machine learning where deep artificial neural networks (DNN) — algorithms inspired by the way neurons work in the brain — find patterns in raw data by combining multiple layers of artificial neurons. As the layers increase, so does the neural network’s ability to learn increasingly abstract concepts.

The simplest kind of DNN is a Multilayer Perceptron (MLP).

• THE LITTLE BOOK OF DEEP LEARNING This book is a short introduction to deep learning for readers with a STEM background, originally designed to be read on a phone screen. It is distributed under a non-commercial Creative Commons license and was downloaded close to 250'000 times in the month following its public release.
• Full Stack Deep Learning Learn Production-Level Deep Learning from Top Practitioners
• DeepLearning.ai a bunch of courses taught by Andrew Ng at Coursera; It's the sequel of Machine Learning course at Coursera.
• Intro to Deep Learning with PyTorch a course by Facebook AI on Udacity
• A friendly introduction to Deep Learning and Neural Networks
• A Neural Network Playground Tinker with a simple neural network designed to help you visualize the learning process
• Deep Learning Demystified - Youtube explain inspiration of deep learning from real neurons to artificial neural networks
• Learn TensorFlow and deep learning, without a Ph.D. This 3-hour course (video + slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain.
• A Guide to Deep Learning by YN^2 a curated maths guide to Deep Learning
• Practical Deep Learning For Coders Course at Fast.ai taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic)
• Deep learning - Udacity recommended for visual learner who knows some ML, this course provides high level ideas of deep learning, dense intuitive details put in a short amount of time, you will use TensorFlow inside the course
• Deep Learning Summer School, Montreal 2015
• Neural networks class - YouTube Playlist
• http://neuralnetworksanddeeplearning.com/index.html a hands-on online book for deep learning maths intuition, I can say that after you finish this, you will be able to explain deep learning in a fine detail.
• The Neural Network Zoo a bunch of neural network models that you should know about (I know about half of them so don't worry that you don't know many because most of them are not popular or useful in the present)
• Intro to TensorFlow for Deep Learning taught at Udacity
• Primers • AI Here’s a hand-picked selection of articles on AI fundamentals/concepts that cover the entire process of building neural nets to training them to evaluating results. There is also a very detailed Transformer architecture explanation.
• Hugging Face Diffusion Models Course Learn the theory, train the model from scratch, and use it to generate images and audio.
• Deep Learning Fundamentals by Lightning.AI with Sebastian Raschka

Convolutional Neural Networks

DNNs that work with grid data like sound waveforms, images and videos better than ordinary DNNs. They are based on the assumptions that nearby input units are more related than the distant units. They also utilize translation invariance. For example, given an image, it might be useful to detect the same kind of edges everywhere on the image. They are sometimes called convnets or CNNs.

• How Convolutional Neural Networks work - Youtube technical explanation including pooling operations, ReLU, fully connected layer, optimization using gradient descent
• Neural Network that Changes Everything - Computerphile
• A Beginner's Guide To Understanding Convolutional Neural Networks
• Deep Learning for Computer Vision (Andrej Karparthy, OpenAI) this is my most favorite video of convolutional net. Andrej explains convnet in detail answering all the curious questions that one might have. For example, most articles only talk about convolution in grayscale image, but he describe convolution in images with color channels as well. He also talks about the concerns and the assumptions that convnets make. This is a great lecture!
• Understanding Neural Networks Through Deep Visualization explains how to visualize a convnet using various techniques

Recurrent Neural Networks

DNNs that have states. They also understand sequences that vary