Deep Learning: Practical Neural Networks with Java

Build and run intelligent applications by leveraging key Java machine learning libraries

Deep Learning: Practical Neural Networks with Java

Yusuke Sugomori et al.

Build and run intelligent applications by leveraging key Java machine learning libraries
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Book Details

ISBN 139781788470315
Paperback744 pages

Book Description

Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.

The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:

1. Java Deep Learning Essentials    
2. Machine Learning in Java
3. Neural Network Programming with Java, Second Edition

Table of Contents

Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders
Chapter 7: Other Important Deep Learning Libraries
Chapter 11: Basic Algorithms – Classification, Regression, and Clustering
Chapter 16: Image Recognition with Deeplearning4j

What You Will Learn

  • Get a practical deep dive into machine learning and deep learning algorithms
  • Explore neural networks using some of the most popular Deep Learning frameworks
  • Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
  • Apply machine learning to fraud, anomaly, and outlier detection
  • Experiment with deep learning concepts, algorithms, and the toolbox for deep learning
  • Select and split data sets into training, test, and validation, and explore validation strategies
  • Apply the code generated in practical examples, including weather forecasting and pattern recognition

Authors

Table of Contents

Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders
Chapter 7: Other Important Deep Learning Libraries
Chapter 11: Basic Algorithms – Classification, Regression, and Clustering
Chapter 16: Image Recognition with Deeplearning4j

Book Details

ISBN 139781788470315
Paperback744 pages
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