TENSOR CALCULUS for Deep Learning (Paperback)

Mir Hossain

ISBN 13: 9798196416347

Published by Independently Published, 2026

About this Item

Paperback. 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 learningThe multivariable chain rule and its role in backpropagationGradient descent, optimization methods, and loss functionsTensor operations including contraction, broadcasting, and einsumThe mathematics behind neural networks, CNNs, RNNs, and transformersHow automatic differentiation engines workAdvanced topics including manifolds and natural gradients Why this book is different: Practical focus: only the math that actually shows up in machine learningStep-by-step solutions with no skipped reasoningWorked examples for every major conceptComplete answers for all exercisesBuilt around real-world frameworks like PyTorch and JAXWho this book is for: College students in data science, AI, or engineeringMachine learning practitioners who want deeper understandingSelf-taught programmers transitioning into AIAnyone 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798196416347

Bibliographic Details

• Title: TENSOR CALCULUS for Deep Learning (Paperback)
• Publisher: Independently Published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798196416347
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.