Mastering-TensorFlow-2.0-A-30-Day-Journey 🚀

Welcome to the "Mastering-TensorFlow-2.0-A-30-Day-Journey" series! This interactive guide is designed to help you master TensorFlow 2.0 in just 30 days. Whether you're new to TensorFlow or an experienced practitioner, this comprehensive tutorial will provide valuable insights and hands-on experience.

About the Series

This series takes you on a step-by-step journey through the powerful world of TensorFlow 2.0. Each day, you'll explore a specific topic or example, starting from the basics and progressing to more advanced concepts. The hands-on approach with Jupyter Notebooks ensures an immersive learning experience.

Syllabus

1. Chapter 1: Modeling Procedure of TensorFlow

• Day 1: Example - Modeling Procedure for Structured Data
• Day 2: Example - Modeling Procedure for Images
• Day 3: Example - Modeling Procedure for Texts
• Day 4: Example - Modeling Procedure for Temporal Sequences

2. Chapter 2: Key Concepts of TensorFlow

• Day 5: Data Structure of Tensor
• Day 6: Three Types of Graph
• Day 7: Automatic Differentiation

3. Chapter 3: Hierarchy of TensorFlow

• Day 8: Low-level API - Demonstration
• Day 9: Mid-level API - Demonstration
• Day 10: High-level API - Demonstration

4. Chapter 4: Low-level API in TensorFlow

• Day 11: Structural Operations of the Tensor
• Day 12: Mathematical Operations of the Tensor
• Day 13: Rules of Using the AutoGraph
• Day 14: Mechanisms of the AutoGraph
• Day 15: AutoGraph and tf.Module

5. Chapter 5: Mid-level API in TensorFlow

• Day 16: Dataset
• Day 17: feature_column
• Day 18: Activation
• Day 19: layers
• Day 20: losses
• Day 21: metrics
• Day 22: optimizers
• Day 23: callbacks

6. Chapter 6: High-level API in TensorFlow

• Day 24: Three Ways of Modeling
• Day 25: Three Ways of Training
• Day 26: Model Training Using Single GPU
• Day 27: Model Training Using Multiple GPUs
• Day 28: Model Training Using TPU
• Day 29: Model Deploying Using TensorFlow-serving
• Day 30: Call TensorFlow Model Using spark-scala

Contributions and Feedback

This project is a collaborative effort, and we welcome contributions and feedback from the community. If you find any issues, have suggestions, or want to improve the content, feel free to create an issue or submit a pull request.

Acknowledgments

A big thank you to lyhue1991 for the tutorial that inspired this series. We also extend our gratitude to the TensorFlow community for their continuous support.

Let's Get Started!

Get ready to dive into the exciting world of TensorFlow! Follow the syllabus, complete each day's chapter, and take your TensorFlow skills to the next level. Happy learning and may your journey be filled with success and growth! 🌟🤖