Book Description

This easy-to-use reference for Tensorflow 2 pattern designs in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.

When and why would you feed training data as NumPy or a streaming dataset? How would you set up cross validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.

  • Understand best practices in Tensorflow model patterns and ML workflows
  • Use code snippets as templates in building TensorFlow models and workflows
  • Save development time by integrating pre-built models in TensorFlow Hub
  • Make informed design choices about data ingestion, training paradigms, model saving, and inferencing
  • Address common scenarios such as model design style, data ingestion workflow, model training, and tuning

Table of Contents

  1. Preface
    1. Conventions Used in This Book
    2. Using Code Examples
    3. O’Reilly Online Learning
    4. How to Contact Us
    5. Acknowledgments
  2. 1. Chapter 2: Data Storage and Ingestion
    1. Streaming data with Python Generators
    2. Streaming file content with a generator
    3. Multiple CSV files as training data
    4. Setting up a pattern for file names
    5. Splitting a single CSV to multiple CSV
    6. Creating a file pattern object using tf.io
    7. Creating a streaming dataset object
    8. Streaming CSV dataset
    9. Organizing image data
    10. Using TensorFlow image generator
    11. Streaming cross-validation images
    12. Inspecting resized images
    13. Summary
  3. 2. Data Preprocessing
    1. Introduction
    2. Preparing tabular data for training
    3. Preparing image data for processing
    4. Preparing text data for processing
    5. Summary