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TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide

By : Holdroyd
2.3 (3)
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TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide

2.3 (3)
By: Holdroyd

Overview of this book

TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
Table of Contents (15 chapters)
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1
Section 1: Introduction to TensorFlow 2.00 Alpha
5
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
7
Unsupervised Learning Using TensorFlow 2
8
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
13
Converting from tf1.12 to tf2

Keras data types

Keras data types (dtypes) are the same as TensorFlow Python data types, as shown in the following table:

Python type Description
tf.float16 16-bit floating point
tf.float32 32-bit floating point
tf.float64 64-bit floating point
tf.int8 8-bit signed integer
tf.int16 16-bit signed integer
tf.int32 32-bit signed integer
tf.int64 64-bit signed integer
tf.uint8 8-bit unsigned integer
tf.string Variable-length byte arrays
tf.bool Boolean
tf.complex64 Complex number made of two 32-bit floating points—one real and imaginary part
tf.complex128 Complex number made of two 64-bit floating points—one real and one imaginary part
tf.qint8 8-bit signed integer used in quantized Ops
tf.qint32 32-bit signed integer used in quantized Ops
tf.quint8 8-bit unsigned integer used in quantized Ops
...

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