Book description
Build and run intelligent applications by leveraging key Java machine learning libraries
About This Book
Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.
Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and realworld applications
This stepbystep guide will help you solve realworld problems and links neural network theory to their application
Who This Book Is For
This course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life.
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
In Detail
Machine learning applications are everywhere, from selfdriving 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:
Java Deep Learning Essentials
Machine Learning in Java
Neural Network Programming with Java, Second Edition
Style and approach
This course aims to create a smooth learning path that will teach you how to effectively use deep learning with Java with other de facto components to get the most out of it. Through this comprehensive course, you’ll learn the basics of predictive modelling and progress to solve realworld problems and links neural network theory to their application
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Table of contents

Deep Learning: Practical Neural Networks with Java
 Table of Contents
 Deep Learning: Practical Neural Networks with Java
 Deep Learning: Practical Neural Networks with Java
 Credits
 Preface

1. Java Deep Learning Essentials
 1. Deep Learning Overview
 2. Algorithms for Machine Learning – Preparing for Deep Learning
 3. Deep Belief Nets and Stacked Denoising Autoencoders
 4. Dropout and Convolutional Neural Networks
 5. Exploring Java Deep Learning Libraries – DL4J, ND4J, and More
 6. Approaches to Practical Applications – Recurrent Neural Networks and More
 7. Other Important Deep Learning Libraries
 8. What's Next?

2. Machine Learning in Java
 1. Applied Machine Learning Quick Start
 2. Java Libraries and Platforms for Machine Learning
 3. Basic Algorithms – Classification, Regression, and Clustering
 4. Customer Relationship Prediction with Ensembles
 5. Affinity Analysis
 6. Recommendation Engine with Apache Mahout
 7. Fraud and Anomaly Detection
 8. Image Recognition with Deeplearning4j
 9. Activity Recognition with Mobile Phone Sensors
 10. Text Mining with Mallet – Topic Modeling and Spam Detection
 11. What is Next?
 A. References

3. Neural Network Programming with Java, Second Edition

1. Getting Started with Neural Networks
 Discovering neural networks

Why artificial neural networks?
 How neural networks are arranged
 The very basic element – artificial neuron
 Giving life to neurons – activation function
 The flexible values – weights
 An extra parameter – bias
 The parts forming the whole – layers
 Learning about neural network architectures
 Monolayer networks
 Multilayer networks
 Feedforward networks
 Feedback networks
 From ignorance to knowledge – learning process
 Let the coding begin! Neural networks in practice
 The neuron class
 The NeuralLayer class
 The ActivationFunction interface
 The neural network class
 Time to play!
 Summary
 2. Getting Neural Networks to Learn
 3. Perceptrons and Supervised Learning

4. SelfOrganizing Maps
 Neural networks unsupervised learning
 Unsupervised learning algorithms

Kohonen selforganizing maps
 Extending the neural network code to Kohonen
 Zerodimensional SOM
 Onedimensional SOM
 Twodimensional SOM
 2D competitive layer
 SOM learning algorithm
 Effect of neighboring neurons – the neighborhood function
 The learning rate
 A new class for competitive learning
 Visualizing the SOMs
 Plotting 2D training datasets and neuron weights
 Testing Kohonen learning
 Summary

5. Forecasting Weather
 Neural networks for regression problems
 Loading/selecting data
 Choosing input and output variables

Preprocessing
 Normalization
 Adapting NeuralDataSet to handle normalization
 Adapting the learning algorithm to normalization
 Java implementation of weather forecasting
 Collecting weather data
 Delaying variables
 Loading the data and beginning to play!
 Let's perform a correlation analysis
 Creating neural networks
 Training and test
 Viewing the neural network output
 Empirical design of neural networks
 Summary
 6. Classifying Disease Diagnosis
 7. Clustering Customer Profiles
 8. Text Recognition
 9. Optimizing and Adapting Neural Networks
 10. Current Trends in Neural Networks

A. References
 Chapter 1: Getting Started with Neural Networks
 Chapter 2: Getting Neural Networks to Learn
 Chapter 3: Perceptrons and Supervised Learning
 Chapter 4: SelfOrganizing Maps
 Chapter 5: Forecasting Weather
 Chapter 6: Classifying Disease Diagnosis
 Chapter 7: Clustering Customer Profiles
 Chapter 8: Text Recognition
 Chapter 9: Optimizing and Adapting Neural Networks
 Chapter 10: Current Trends in Neural Networks
 Bibliography

1. Getting Started with Neural Networks
 Index
Product information
 Title: Deep Learning: Practical Neural Networks with Java
 Author(s):
 Release date: June 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788470315
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