This repository is a comprehensive, curated list of resources for TensorFlow, a popular open-source library for numerical computation and deep learning. It serves as a valuable starting point for developers, researchers, and students looking to explore TensorFlow's capabilities, find relevant projects, and learn best practices.

How It Works

The list is organized into categories such as tutorials, models/projects, libraries, tools, videos, papers, blogs, and books. It aggregates links to official documentation, community-contributed examples, research papers, and practical implementations, providing a broad overview of the TensorFlow ecosystem.

Quick Start & Requirements

• Installation: TensorFlow itself can be installed via pip (pip install tensorflow). However, this list focuses on projects using TensorFlow, each with its own installation and dependency requirements.
• Prerequisites: Many projects leverage TensorFlow's GPU acceleration, requiring NVIDIA GPUs with compatible CUDA and cuDNN versions. Python 3.x is standard. Specific projects may require large datasets, cloud platforms, or specialized hardware.
• Resources: Links to official TensorFlow documentation, tutorials, and community forums are provided for deeper dives.

Highlighted Details

• Extensive coverage of various deep learning architectures (CNNs, RNNs, GANs, Transformers) and applications (image recognition, NLP, audio generation, reinforcement learning).
• Includes resources for deploying TensorFlow models on edge devices (e.g., Raspberry Pi, Android) and integrating with distributed systems (e.g., Spark).
• Features links to academic papers, official blog posts, and books for both theoretical understanding and practical application.
• Highlights tools for optimization, visualization (TensorBoard), and managing ML workflows (Kubeflow, Guild AI).

Maintenance & Community

• The list is maintained by jtoy and welcomes community contributions via pull requests.
• Links to official TensorFlow community channels (Stack Overflow, Twitter, Reddit, mailing lists) are provided.

Licensing & Compatibility

• TensorFlow itself is typically released under the Apache 2.0 license, allowing for commercial use.
• The licenses of individual projects linked within the list vary and should be checked separately.

Limitations & Caveats

• As a curated list, the quality and maintenance status of individual linked projects can vary. Some older projects may not be compatible with the latest TensorFlow versions or best practices.