This repository provides the code and Jupyter notebooks for the book "Deep Learning Illustrated" by Jon Krohn, Grant Beyleveld, and Aglaé Bassens. It serves as a visual and interactive guide to artificial neural networks, targeting individuals seeking to understand and implement deep learning concepts. The primary benefit is a hands-on approach to learning, demystifying complex topics through code examples.

How It Works

The project's core is a collection of Jupyter notebooks, each corresponding to a chapter in the book. These notebooks implement various deep learning architectures and techniques, from basic neural networks and activation functions to advanced topics like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning. The approach emphasizes illustrating theory with practical code, often using Keras and TensorFlow.

Quick Start & Requirements

• Installation: Step-by-step guides are available in the installation directory.
• Prerequisites: Python, Jupyter Notebooks, TensorFlow (with notes on compatibility with TensorFlow 2.x). Specific library versions are detailed in the installation guides.
• Resources: Notebooks cover a range of complexity, from simple network implementations to more resource-intensive tasks like training GANs or RL agents.
• Links: Installation Guides, TensorFlow 2.x Analogs

Highlighted Details

• Comprehensive coverage of deep learning theory and practice, aligned with a published book.
• Interactive Jupyter notebooks for hands-on learning and experimentation.
• Examples span computer vision, natural language processing, generative models, and reinforcement learning.
• Includes code for classic architectures like LeNet-5, AlexNet, and modern techniques like LSTMs and GANs.

Maintenance & Community

The project is associated with the book "Deep Learning Illustrated." For installation difficulties, users are directed to the book's Q&A forum rather than creating GitHub Issues.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README. Users should verify licensing for commercial use or integration into closed-source projects.

Limitations & Caveats

The code was developed with TensorFlow 2.0 released after the book's publication, though notes indicate straightforward conversion to TensorFlow 2.x. Some notebooks may require significant computational resources for training.