Overview

Master the mathematics behind modern AI without getting lost in theory. Most deep learning books either skip the math or bury you in abstract theory. Tensor Calculus for Deep Learning bridges that gap, giving you exactly the mathematical tools you need to understand, build, and debug real machine learning models. Whether you're a student, engineer, or self-taught practitioner, this book takes you from core linear algebra and multivariable calculus to the tensor operations that power neural networks step by step, with clarity and purpose. You will learn how gradients flow through networks, how backpropagation really works, and how optimization algorithms shape model performance, all through the lens of tensor calculus. What you will learn: How vectors, matrices, and tensors connect in deep learning The multivariable chain rule and its role in backpropagation Gradient descent, optimization methods, and loss functions Tensor operations including contraction, broadcasting, and einsum The mathematics behind neural networks, CNNs, RNNs, and transformers How automatic differentiation engines work Advanced topics including manifolds and natural gradients Why this book is different: Practical focus: only the math that actually shows up in machine learning Step-by-step solutions with no skipped reasoning Worked examples for every major concept Complete answers for all exercises Built around real-world frameworks like PyTorch and JAX Who this book is for: College students in data science, AI, or engineering Machine learning practitioners who want deeper understanding Self-taught programmers transitioning into AI Anyone who wants to read research papers with confidence If deep learning has ever felt like a black box, this book will give you the mathematical clarity to understand what is really happening inside.

Full Product Details

9798196416347

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available