TFLite's capability of being able to run on low-power and low-binary machines makes it very powerful when run on embedded Linux machines. TFLite can be run on many of the popular embedded Linux machines, and as well as on the Coral Dev Board. In this section, we will cover the building, compiling, and running of TFLite on three devices. The first device that's covered is the Coral Dev Board with the Edge TPU processor, the second device is the NVIDIA Jetson Nano, and the final one is the Raspberry Pi. The NVIDIA Jetson Nano is a small and powerful computer from NVIDIA that runs multiple neural networks in parallel in applications such as image classification, object detection, segmentation, and speech processing.
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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