Book Image

Deep Learning with Keras

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras

By: Antonio Gulli, Sujit Pal

Overview of this book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Installing Keras on Google Cloud ML

Installing Keras on Google Cloud is very simple. First, we can install Google Cloud (for the downloadable file, refer to, acommand-line interface for Google Cloud Platform; then we can use CloudML, a managed service that enables us to easily build machine, learning models with TensorFlow. Before using Keras, let's use Google Cloud with TensorFlow to train an MNIST example available on GitHub. The code is local and training happens in the cloud:

In the following screenshot, you can see how to run a training session:

We can use TensorBoard to show how cross-entropy decreases across iterations:

In the next screenshot, we see the graph of cross-entropy:

Now, if we want to use Keras on the top of TensorFlow, we simply download the Keras source from PyPI (for the downloadable file, refer to or later versions) and then directly use Keras as a CloudML package solution, as in the following example...