This repository provides code examples and explanations for the concepts covered in the book "Grokking Deep Learning." It is designed for individuals seeking to understand the fundamental principles of deep learning through practical implementation, covering topics from basic neural network mechanics to advanced architectures like CNNs and RNNs.

How It Works

The project implements deep learning concepts from scratch using Python, focusing on building foundational understanding without relying heavily on high-level libraries. It demonstrates core algorithms like forward propagation, gradient descent, and backpropagation, illustrating how neural networks learn and generalize.

Quick Start & Requirements

• Primary install / run command: python <filename>.py (specific files vary by chapter).
• Prerequisites: Python 3.x, NumPy.
• Links: Book, Coupon Code

Highlighted Details

• Covers foundational concepts from forward propagation to backpropagation.
• Includes introductions to CNNs, word embeddings, RNNs, LSTMs, and federated learning.
• Demonstrates learning signal vs. noise with regularization.
• Explains automatic differentiation and exploding gradients.

Maintenance & Community

• Maintained by Andrew Trask.
• No explicit community links (Discord, Slack) or roadmap provided in the README.

Licensing & Compatibility

• License not specified in the README.
• Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

The repository is tied to a specific book's curriculum and may not represent the most current or optimized deep learning practices. The lack of specified licensing could pose issues for commercial adoption.