This repository serves as a curated directory of tutorials, code examples, and third-party libraries for Keras, the Python deep learning library. It aims to provide a comprehensive resource for users of all levels, from beginners to advanced practitioners, looking to learn and implement various deep learning models and techniques.

How It Works

The repository is organized into categories such as "Official starter resources," "Tutorials," "Code examples," "Projects built with Keras," and "Third-party libraries." This structure allows users to easily navigate and find relevant materials for specific tasks, from basic model building to advanced applications like image captioning, reinforcement learning, and style transfer. The inclusion of numerous code examples and links to external resources facilitates hands-on learning and practical implementation.

Quick Start & Requirements

This repository is a collection of links and code snippets, not a single installable package. To run the provided code examples, users will need to have Python and Keras installed, along with other libraries specified within individual tutorial or example repositories (e.g., TensorFlow, NumPy, Scikit-learn, Matplotlib). Many examples may also benefit from GPU acceleration for faster training.

Highlighted Details

• Extensive collection of tutorials covering diverse deep learning tasks: image classification, text processing, autoencoders, reinforcement learning, and more.
• Numerous code examples demonstrating popular architectures like CNNs, LSTMs, GANs, and ResNets, often with pre-trained weights.
• Links to third-party libraries that extend Keras functionality for hyperparameter optimization, visualization, and browser deployment.
• Showcases real-world projects built with Keras, including implementations of research papers and competitive AI agents.

Maintenance & Community

The repository is maintained by François Chollet, the creator of Keras. Contributions are encouraged via pull requests. Links to official Keras documentation and community forums are provided.

Licensing & Compatibility

The licensing for individual code examples and tutorials may vary, as they are often sourced from different projects. Users should check the specific licenses of any code they intend to use. Keras itself is typically released under a permissive license (e.g., Apache 2.0), allowing for commercial use.

Limitations & Caveats

As a curated directory, the quality and maintenance status of linked external resources can vary. Some older examples might rely on deprecated Keras or TensorFlow APIs, requiring updates for compatibility with current versions.