Book Image

Python Deep Learning

By : Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants
Book Image

Python Deep Learning

By: Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (18 chapters)
Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


It should be noted, as it may have become clear, that there is no general architecture for a convolutional neural network. However, there are general guidelines. Normally, pooling layers follow convolutional layers, and often it is customary to stack two or more successive convolutional layers to detect more complex features, as it is done in the VGG-16 neural net example shown earlier. Convolutional networks are very powerful. However, they can be quite resource-heavy (the VGG-16 example above, for example, is relatively complex), and usually require a long training time, which is why the use of GPU can help speed up performance. Their strength comes from the fact that they do not focus on the entire image, rather they focus on smaller sub-regions to find interesting features that make up the image in order to be able to find discriminating elements between different inputs. Since convolutional layers are very resource-heavy, we have introduced pooling layers that help reduce the...