2-week speedrun to machine learning mastery for either an engineer or researcher path.

A visualization of an MNIST neural network written from scratch in Odin and Raylib. This project provides a 3D visualisation of how a neural network works for the famous handwriting detection problem.

What's actually happening to a neural network as it learns? Grant discusses back propagation, a key algorithm behind how neural networks learn, using the example of recognizing handwritten numbers.

What are the neurons, why are there layers, and what is the math underlying it? Understanding and adjusting neural network structures and functions is crucial for effective pattern recognition and learning.

Software 2.0 signifies a foundational shift in how software is developed using neural networks.

A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models.

LSTMs are crucial for learning long-term dependencies in recurrent neural networks.

A book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.

A 12-week, 24-lesson curriculum for Artificial Intelligence from Microsoft. It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI.

Learn the basics of neural networks and backpropagation, one of the most important algorithms for the modern world.

Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.

This MIT course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Everything you need to know, from the basics, to image classification with Convolutional Neural Networks, to the state of the art transformers used for large language models like ChatGPT, presented one step at a time so that it is easily understood.

Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.

Andrej presents a comprehensive lecture on training deep neural networks, explaining the creation and training of a neural network using his library, micrograd, and comparing it to PyTorch. Understanding and training neural networks requires mastering concepts like backpropagation, loss functions, and gradient descent. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.