Linear Algebra And Learning From Data By: Gilbert Strang ((better))

Unlike many AI books that treat libraries like PyTorch or TensorFlow as "black boxes," Strang forces you to look at the of the data. He explains why a weight matrix behaves the way it does and how the chain rule translates into the backpropagation algorithm. Key Topics Covered

If you are new to linear algebra, read Strang’s Introduction to Linear Algebra first, then return to Learning from Data . linear algebra and learning from data by gilbert strang

This final part covers topics essential for large-scale computation, which classical linear algebra courses often omit. Unlike many AI books that treat libraries like

Learning from data is essentially an optimization problem. Strang covers how we navigate a "loss surface" to find the minimum error. He introduces , the engine that allows us to train models on millions of data points without crashing our computers. 4. Probability and Statistics This final part covers topics essential for large-scale