A running collection of the books, courses, hands-on practice, and tools I actually use for quantitative finance, deep learning, and working with large datasets. I keep adding to this as I find things worth sharing.
Notes on Andrej Karpathy's micrograd — a ~150-line autograd engine that builds backpropagation, gradient descent, and a full neural net (Value, Neuron, Layer, MLP) from first principles. Still the best free, intuitive explanation of how neural nets actually learn.