Название: TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models (Final)
Автор: KC Tung
Издательство: O’Reilly Media, Inc.
Год: 2021
Страниц: 255
Язык: английский
Формат: pdf (true), epub
Размер: 12.2 MB, 11.4 MB

This easy-to-use reference for TensorFlow 2 design patterns 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 using 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.

The TensorFlow ecosystem has evolved into many different frameworks to serve a variety of roles and functions. That flexibility is part of the reason for its widespread adoption, but it also complicates the learning curve for data scientists, machine learning (ML) engineers, and other technical stakeholders. There are so many ways to manage TensorFlow models for common tasks—such as data and feature engineering, data ingestions, model selection, training patterns, cross validation against overfitting, and deployment strategies—that the choices can be overwhelming.

This book is written for readers with basic experience in and knowledge about building ML models. Some proficiency in Python programming is highly recommended. If you work through the book from beginning to end, you will gain a great deal of knowledge about the end-to-end model development process and the major tasks involved, including data engineering, ingestion, and preparation; model training; and serving the model.

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 prebuilt 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