TensorFlow 2.0 Quick Start Guide

More Information
  • 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

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.

  • Train your own models for effective prediction, using high-level Keras API
  • Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks
  • Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha
Page Count 196
Course Length 5 hours 52 minutes
ISBN 9781789530759
Date Of Publication 29 Mar 2019


Tony Holdroyd

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.