When we introduced the idea of deep learning, we discussed how the word "deep" refers not only to the fact that we use many layers in our neural net, but also to the fact that we have a "deeper" learning process. Part of this deeper learning process was the ability of the neural net to learn features autonomously. In the previous section, we defined specific filters to help the network learn specific characteristics. This is not necessarily what we want. As we discussed, the point of deep learning is that the system learns on its own, and if we had to teach the network what features or characteristics are important, or how to learn to recognize digits by applying layers such as the edges layer that highlights the general shape of a digit, we would be doing most of the work and possibly constraining the network to learn features that may be relevant to us but not to the network, degrading its performance. The point of Deep Learning is that the system...
Python Deep Learning
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Python Deep Learning
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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.
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Table of Contents (18 chapters)
Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Machine Learning – An Introduction
Neural Networks
Deep Learning Fundamentals
Unsupervised Feature Learning
Image Recognition
Recurrent Neural Networks and Language Models
Deep Learning for Board Games
Deep Learning for Computer Games
Anomaly Detection
Building a Production-Ready Intrusion Detection System
Index
Customer Reviews