This machine learning book series aims at providing real hands-on training from general concepts and architecture to low-level details and mathematics. The first epoch covers the simplest linear associative network, proposes a brick notation for algebraic expressions, shows required calculus derivations, and illustrates gradient descent. Subsequent epochs also include necessary computer science foundations, statistics, data science topics, algorithms, and data structures.
