LiteRT overview

 TensorFlow Guide

Optimized for on-device machine learning : LiteRT addresses five key ODML constraints: latency (there's no round-trip to a server), privacy (no personal data leaves the device), connectivity (internet...

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 TensorFlow Tutorials

An open source machine learning library for research and production.

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 TensorFlow Tutorials

Learn basic and advanced concepts of TensorFlow such as eager execution, Keras high-level APIs and flexible model building.

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TF-NumPy Type Promotion

 TensorFlow Guide

There are 4 options for type promotion in TensorFlow. By default, TensorFlow raises errors instead of promoting types for mixed type operations. Running tf.numpy.experimental_enable_numpy_behavior() s...

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Customizing Saving and Serialization

 TensorFlow Guide

A more advanced guide on customizing saving for your layers and models.

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Keras: The high-level API for TensorFlow

 TensorFlow Guide

Introduction to Keras, the high-level API for TensorFlow.

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The Functional API

 TensorFlow Guide

Complete guide to the functional API.

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Training & evaluation with the built-in methods

 TensorFlow Guide

Complete guide to training & evaluation with `fit()` and `evaluate()`.

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Serialization and saving

 TensorFlow Guide

Complete guide to saving & serializing models.

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Making new layers and models via subclassing

 TensorFlow Guide

Complete guide to writing `Layer` and `Model` objects from scratch.

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Understanding masking & padding

 TensorFlow Guide

Complete guide to using mask-aware sequence layers in Keras.

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Working with RNNs

 TensorFlow Guide

Complete guide to using & customizing RNN layers.

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