Deep Learning From Scratch : Building with Python From First Principles

Description

Deep Learning From Scratch: Building with Python From First Principles is a practical and conceptual guide for developers, students, and data scientists who want to truly understand how deep learning works under the hood.

Instead of relying on black-box libraries, this book walks readers through building deep learning components from first principles using Python. You’ll learn the mathematical and computational foundations of neural networks, including forward propagation, backpropagation, gradient descent, and optimization techniques.

Through step-by-step explanations and hands-on implementations, the book covers core deep learning concepts such as multilayer perceptrons, activation functions, loss functions, and training neural networks from scratch. By implementing models manually, readers gain deep intuition into how modern deep learning systems operate.

Ideal for learners who want more than surface-level knowledge, this book bridges theory and practice, making it perfect for those preparing for advanced AI work or seeking to strengthen their fundamentals before using high-level frameworks like TensorFlow or PyTorch.

Key highlights include:
Deep learning fundamentals explained from first principles
Building neural networks using pure Python
Understanding backpropagation and gradient-based learning
Ideal for students, developers, and AI practitioners