Jason Brownlee's long-running tutorial site: hundreds of free, step-by-step machine learning walkthroughs in Python, organised into 'start here' guides by topic — getting set up, understanding algorithms, your first complete project, your first neural network, time series forecasting. Each tutorial is written to get you to a working result rather than a theory exam.
Why I recommend it: The tutorials and the 'start here' guides are free to read with no account. The site's business is paid ebooks, and the free ebook offer costs you an email address and an ongoing email course, so expect the marketing. A fair criticism to know going in: the tutorials are recipe-shaped, which gets you running code fast but can leave the why thin — pair them with something that explains the ideas.
Google Scholar profile listing Geoffrey Hinton's papers in citation order — backpropagation, dropout, AlexNet, t-SNE and the rest of the deep learning canon.
From the site: Emeritus Prof. Computer Science, University of Toronto - Cited by 1.089.325 - machine learning - psychology - artificial intelligence - cognitive science - computer science
Why I recommend it: The single best index of the papers that made current AI work. Sort by year to see the ideas arrive. Scholar blocks automated visits, so the picture may be a screenshot.
