This repository offers an open-source book on Deep Learning with TensorFlow 2.0, targeting beginners and those seeking practical, code-driven learning. It provides a comprehensive resource combining theoretical explanations with hands-on TensorFlow 2.0 case studies, aiming to facilitate rapid AI skill acquisition.

How It Works

The book integrates theoretical concepts with practical TensorFlow 2.0 implementations, presented through code examples and IPython Notebooks. This approach allows learners to directly apply learned principles, fostering a deeper understanding of deep learning techniques within the TensorFlow framework.

Quick Start & Requirements

• Installation: Download the PDF and source code from the repository or Baidu Netdisk (extraction code: juqs).
• Prerequisites: TensorFlow 2.0, Python. Specific hardware requirements are not detailed but are typical for deep learning tasks.
• Resources: Links to paper book purchase, Baidu Netdisk download, and accompanying video courses are provided.

Highlighted Details

• Comprehensive coverage of Deep Learning with TensorFlow 2.0.
• Includes PDF ebook, source code, and courseware.
• Features IPython Notebooks for enhanced code interaction.
• Recognized by major AI media outlets and adopted by numerous universities.

Maintenance & Community

The project has seen significant community engagement, achieving top global rankings on GitHub trends. Feedback and contributions are encouraged via GitHub Issues. Contact is available via email for specific inquiries and academic resources.

Licensing & Compatibility

Content usage is restricted to non-commercial purposes, requiring attribution to the author and GitHub link.

Limitations & Caveats

The primary limitation is the non-commercial use restriction. While comprehensive, the repository's focus is on TensorFlow 2.0, and specific hardware or performance benchmarks are not explicitly detailed.