Neural Networks and Deep Learning
Andrew Ng
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
More resources on AI Fundamentals
AI Superpowers
A rigorous geopolitical and economic analysis of the race for AI dominance and its structural impact on global labor markets.
Practical Deep Learning for Coders
fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library.
Distill.pub
Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes.
StatQuest with Josh Starmer - Neural Networks videos
Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.'
3Blue1Brown - Neural Networks series
Grant Sanderson's 3Blue1Brown channel offers visually intuitive and mathematically rigorous explanations of complex topics. His series on neural networks is particularly praised for making the core concepts, like backpropagation, understandable through animated visuals.
Artificial Intelligence (MIT 6.034)
Covers knowledge representation, search, constraint satisfaction, rule-based systems, neural nets, support vector machines and boosting. Includes 30 lecture videos, problem-solving videos, programming assignments and exams. Afterwards you can build simple intelligent systems and reason about how machines solve problems and learn.