Smartphone use has grown exponentially over the last few years and continues to grow in an unabated fashion. Other IoT devices are also becoming increasingly commonplace in our day-to-day lives. These upward trends in usage adoption have interesting consequences for machine learning systems. These platforms are typically resource-constrained in comparison to normal host machines. As a result, additional optimizations are required to run inference on such devices. The TensorFlow platform supports building machine learning and deep learning-based applications that can run on different kinds of edge devices such as mobile phones and other IoT devices. The primary tool made available to this effect is the TensorFlow Lite platform. TensorFlow Lite can be used to build machine learning applications for a variety of platforms including...
What's New in TensorFlow 2.0
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What's New in TensorFlow 2.0
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Overview of this book
TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features.
What's New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You'll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis.
By the end of the book, you'll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.
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Current Title:
What's New in TensorFlow 2.0
Table of Contents (13 chapters)
Title Page
Copyright and Credits
Contributors
About Packt
Preface
Free Chapter
Getting Started with TensorFlow 2.0
Keras Default Integration and Eager Execution
Designing and Constructing Input Data Pipelines
Model Training and Use of TensorBoard
Model Inference Pipelines - Multi-platform Deployments
AIY Projects and TensorFlow Lite
Migrating From TensorFlow 1.x to 2.0
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Customer Reviews