Deep Learning by Code: Build Neural Networks from Scratch

Build Neural Networks from the Ground Up-No Black Boxes, Just CodeIf you've ever wanted to truly understand how deep learning works-not just use high-level libraries-Deep Learning by Code is your roadmap.This hands-on guide teaches you how to implement the core building blocks of neural networks from scratch using pure Python and NumPy. You'll code every layer, activation function, and optimization algorithm yourself-gaining a deep, intuitive understanding of how modern AI really works under the hood.Whether you're a developer, data science student, or aspiring machine learning engineer, this book gives you the confidence and skills to create and experiment with your own deep learning models-line by line.Inside You'll Learn: How neural networks process information, learn patterns, and make predictionsStep-by-step construction of forward and backward propagationImplementing activation functions, loss functions, and optimizers by handBuilding feedforward, convolutional, and recurrent neural networksUnderstanding gradient descent, backpropagation, and weight updatesCreating training loops without relying on frameworksVisualizing training behavior and model accuracyTransitioning from raw code to real-world applicationsBonus: Compare your custom models to PyTorch and TensorFlowThis isn't just another tutorial-it's a deep dive into the mechanics of deep learning. You'll leave not just knowing how, but why.

• | Author: Thompson Carter
• | Publisher: Independently Published
• | Publication Date: Jun 28, 2025
• | Number of Pages: 00240 pages
• | Binding: Paperback or Softback
• | ISBN-10: NA
• | ISBN-13: 9798290086538