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The Maths of DeepLearning
Understanding Gradient Descent and Backpropagation from First Principles
Unlock the black box of Deep Learning. This book takes you on a journey from the humble dot product to the elegant complexity of Backpropagation via Matrix Calculus. No magic, just math and code. Perfect for developers who want to understand the 'why' and 'how' behind the equations, derived step-by-step from first principles.
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About
About the Book
Unlock the black box of Deep Learning. This book takes you on a journey from the fundamental building blocks of vectors and dot products to the sophisticated calculus behind backpropagation and neural network optimization.
Unlike other guides that rely on high-level libraries, this book focuses on the why and how. You will build your intuition step-by-step:
- Master the Basics: Understand how simple Perceptrons work using vectors and hyperplanes.
- Demystify Optimization: Visualize Gradient Descent and see how machines "learn".
- Crack Backpropagation: Derive the core algorithms of Deep Learning from scratch, first using the Chain Rule and then mastering Matrix Calculus.
- Build from Scratch: Implement a neural network in Python without relying on "magic" frameworks, ensuring you truly understand the code.
- Advanced Concepts: Dive into Softmax, Cross-Entropy Loss, and the matrix operations that power modern AI.
Perfect for developers, students, and enthusiasts who want to move beyond and truly grasp the mathematics driving the AI revolution.
Author
About the Author
Alex Carmel Punnen
Alex Punnen is a software architect and technologist with over 20 years of experience in designing, building, and evolving large-scale software systems. His career spans multiple generations of computing—from early distributed systems and object-oriented middleware to modern cloud-native architectures, microservices, and containerized platforms.
A polyglot programmer with a strong systems mindset, Alex has worked across the full technology stack, including low-level protocols, distributed data platforms, and high-performance backend systems. He is driven by a deep curiosity for first principles—understanding not just how systems work, but why they work.
In recent years, this curiosity has led him into Artificial Intelligence and Deep Learning, with a particular focus on the mathematics that underpins neural networks. Rather than treating AI as a black box, Alex approaches it as a composition of linear algebra, calculus, probability, and optimization.
This book reflects that philosophy. The Maths of Deep Learning is written for engineers and learners who want to move beyond intuition and buzzwords, and instead build a clear, rigorous understanding of deep learning—one equation, one idea, and one insight at a time.
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