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The Tensor Cookbook (2024)
The Tensor Cookbook is a comprehensive guide to tensors, using the visual language of tensor diagrams. It closely follows the legendary 'Matrix Cookbook' while providing a new way to understand and appreciate tensor operations through diagrams.
Machine learning involves a lot of tensor manipulation, and it's easy to lose track of the bigger picture when manipulating high-dimensional data using notation designed for vectors and matrices. Make it easy to spot patterns and symmetries Avoid all trouble with vectorization and Kronecker products Make Matrix Calculus simple and intuitive Represent functions and broadcasting effortlessly b, x, y = sp.symbols("b x y") X = tg.Variable("X", b, x) Y = tg.Variable("Y", b, y) W = tg.Variable("W", x, y) error = X @ W - Y loss = error @ error expr = tg.Derivative(loss, W) save_steps(expr) Simplify Step by Step Introduction to tensor diagrams Simple and advanced derivatives Statistics and probability Kronecker and Vec operators Special matrices and decompositions Machine learning applications And much more!
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