This is the code repository for TensorFlow 2.0 Quick Start Guide, published by Packt.

Get up to speed with the newly introduced features of TensorFlow 2.0

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.

This book covers the following exciting features:

• Use tf.Keras for fast prototyping, building, and training deep learning neural network models
• Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files
• Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications
• Understand image recognition techniques using TensorFlow
• Perform neural style transfer for image hybridization using a neural network
• Code a recurrent neural network in TensorFlow to perform text-style generation

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

image1 = tf. zeros([ 7, 28, 28, 3]) #  example-within-batch by height by
width by color

Following is what you need for this book: Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.

With the following software and hardware list you can run all code files present in the book (Chapter 1-9).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Deep Learning with TensorFlow - Second Edition [Packt] [Amazon]

• Intelligent Mobile Projects with TensorFlow [Packt] [Amazon]

Tony Holdroyd's first degree, from Durham University, was in maths and physics. He also has technical qualifications, including MCSD, MCSD.net, and SCJP. He holds an MSc in computer science from London University. He was a senior lecturer in computer science and maths in further education, designing and delivering programming courses in many languages, including C, C+, Java, C#, and SQL. His passion for neural networks stems from research he did for his MSc thesis. He has developed numerous machine learning, neural network, and deep learning applications, and has advised in the media industry on deep learning as applied to image and music processing. Tony lives in Gravesend, Kent, UK, with his wife, Sue McCreeth, who is a renowned musician.

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