christoschristofidis/awesome-deep-learning

This repository is a curated list, what GitHub calls an "awesome list", focused on deep learning. Deep learning is the family of machine learning techniques built around neural networks with many layers, the technology behind things like image recognition, speech recognition, and modern AI assistants. Rather than being software you install and run, this repo is a long, organized bibliography of resources for learning the field. The README is structured as a table of contents that groups links into sections: Books, Courses, Videos and Lectures, Papers, Tutorials, Researchers, Websites, Datasets, Conferences, Frameworks, Tools, Miscellaneous, and Contributing. Inside each section you get a numbered list of named items with links pointing out to the original source. For example the Books section lists titles like "Deep Learning" by Bengio, Goodfellow and Courville, "Neural Networks and Deep Learning" by Michael Nielsen, "Dive into Deep Learning," and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow." The Courses section collects university lectures and online classes, including Andrew Ng's Stanford machine learning course, Geoffrey Hinton's neural networks course, Stanford's CS231n on convolutional networks, fast.ai's Practical Deep Learning for Coders, MIT's introduction to deep learning, and OpenAI's Spinning Up in Deep Reinforcement Learning. You would use this when you are trying to learn deep learning, teach it, or build a reading list, and you want a single starting place that points to vetted external resources instead of having to search them out one by one. Because it is a markdown list of links rather than a program, there is no real tech stack, it is just the README itself, with a contributing section so others can suggest more entries. The full README is longer than what was provided.