Mathematics Behind Neural Networks 400 Illustrated Exercises from Algebra to Transformers

Mathematics Behind Neural Networks 400 Illustrated Exercises from Algebra to Transformers | 98.2 MB

Title: Mathematics Behind Neural Networks: 400 Illustrated Exercises from Algebra to Transformers
Author: T Aadhya
Category: Linear Algebra, Linear Algebra, Neural Networks
Language: English | 1294 Pages | ISBN: B0GXX4QN4F

Description:
Mathematics Behind Neural Networks: 400 Illustrated Exercises from Algebra to Transformers is a hands on workbook for readers who want to understand the mathematics inside modern neural networks, one computation at a time.
If you have ever seen a neural network diagram and wanted to know what the numbers are actually doing, this book gives you the answer through worked exercises, clear notation, and structured practice. From scalars and vectors to matrix multiplication, activation functions, backpropagation, optimization, convolutional networks, recurrent networks, embeddings, and transformers, each chapter breaks the subject into concrete calculations you can perform by hand.
This book is designed to help readers move beyond abstract explanations and build real mathematical fluency. Every problem asks you to compute something specific: a dot product, a forward pass, a loss value, a gradient, an attention weight, or a parameter update. The goal is not to guess the concept, but to calculate it clearly and correctly.
Inside this workbook, you will find:

• 400 illustrated exercises arranged across 21 chapters and 6 parts.
• 3 difficulty levels: Beginner, Intermediate, and Expert.
• Step by step solutions for every problem.
• A complete linear algebra foundation built from the ground up.
• Coverage of derivatives, gradients, backpropagation, optimization, loss functions, probability, statistics, CNNs, RNNs, embeddings, and transformers.
• A problem driven format that supports self study, classroom use, and technical review.

The workbook assumes comfort with high school algebra and basic calculus, but no prior machine learning knowledge is required. The first chapters build the necessary foundations, while later chapters move into the mathematics of deep learning systems and transformer models.
This book is ideal for:

• Students who want to move from theory to computation.
• Practitioners who can use neural network libraries but want stronger mathematical understanding.
• Researchers and technical readers who want a clear reference for core operations.

If you want to understand what neural networks are doing mathematically, this workbook gives you a direct path from the simplest operations to the most advanced models.

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