Author: Tony Holdroyd
Pub Date: 2019
Size: 37 Mb
Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.
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.
What you will learn
- 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