Deep Learning: Practical Neural Networks with Java

Name: Deep Learning: Practical Neural Networks with Java | Build and run intelligent applications by leveraging key Java machine learning libraries
Brand: Packt Publishing
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Build and run intelligent applications by leveraging key Java machine learning libraries

Alan M. F. Souza Fabio M. Soares Yusuke Sugomori(Author)

Packt Publishing

Published on 15. April 2025

744 pages

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978-1-78847-171-8 (ISBN)

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Souza Alan M. F. :
Alan M. F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM). He holds a post-graduate degree in project management software and a master's degree in industrial processes (applied computing) from Universidade Federal do Para (UFPA). He has been working with neural networks since 2009 and has worked with Brazilian IT companies developing in Java, PHP, SQL, and other programming languages since 2006. He is passionate about programming and computational intelligence. Currently, he is a professor at Universidade da Amazonia (UNAMA) and a PhD candidate at UFPA. M. Soares Fabio :

Fbio M. Soares is currently a PhD candidate at the Federal University of Par (Universidade Federal do Par - UFPA), in northern Brazil. He is very passionate about technology in almost all fields, and designs neural network solutions since 2004 and has applied this technique in several fields like telecommunications, industrial process control and modeling, hydroelectric power generation, financial applications, retail customer analysis and so on. His research topics cover supervised learning for data-driven modeling. As of 2017, he is currently carrying on research projects with chemical process modeling and control in the aluminum smelting and ferronickel processing industries, and has worked as a lecturer teaching subjects involving computer programming and artificial intelligence paradigms. As an active researcher, he has also a number of articles published in English language in many conferences and journals, including four book chapters.Sugomori Yusuke :

Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users' respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot of attention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015. In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight "new generation" creators by the Japanese magazine Web Designing. In April 2016, he joined a medical start-up as cofounder and CTO.

Content

Cover
Preface
Table of Contents
Module 1
Chapter 1: Deep Learning Overview
Transition of AI
Things dividing a machine and human
AI and deep learning
Summary
Chapter 2: Algorithms for Machine Learning - Preparing for Deep Learning
Getting started
The need for training in machine learning
Supervised and unsupervised learning
Machine learning application flow
Theories and algorithms of neural networks
Summary
Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders
Neural networks fall
Neural networks' revenge
Deep learning algorithms
Summary
Chapter 4: Dropout and Convolutional Neural Networks
Deep learning algorithms without pre-training
Dropout
Convolutional neural networks
Summary
Chapter 5: Exploring Java Deep Learning Libraries - DL4J, ND4J, and More
Implementing from scratch versus a library/framework
Introducing DL4J and ND4J
Implementations with ND4J
Implementations with DL4J
Summary
Chapter 6: Approaches to Practical Applications - Recurrent Neural Networks and More
Fields where deep learning is active
The difficulties of deep learning
The approaches to maximizing deep learning possibilities and abilities
Summary
Chapter 7: Other Important Deep Learning Libraries
Theano
TensorFlow
Caffe
Summary
Chapter 8: What's Next?
Breaking news about deep learning
Expected next actions
Useful news sources for deep learning
Summary
Module 2: Machine Learning in Java
Chapter 1: Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
Chapter 2: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 3: Basic Algorithms - Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 4: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 5: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 6: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 7: Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
Chapter 8: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 9: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 10: Text Mining with Mallet - Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
Chapter 11: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
References
Module 3
Chapter 1: Getting Started with Neural Networks
Discovering neural networks
Why artificial neural 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
Chapter 2: Getting Neural Networks to Learn
Learning ability in neural networks
Learning paradigms
The learning process
Examples of learning algorithms
Time to see the learning in practice!
Amazing, it learned! Or, did it really? A further step - testing
Summary
Chapter 3: Perceptrons and Supervised Learning
Supervised learning - teaching the neural net
A basic neural architecture - perceptrons
Multi-layer perceptrons
Learning in MLPs
Practical example 1 - the XOR case with delta rule and backpropagation
Practical example 2 - predicting enrolment status
Summary
Chapter 4: Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
Chapter 5: Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
Chapter 6: Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 7: Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
Chapter 8: Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
Chapter 9: Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
Chapter 10: Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary
References
Bibliography
Index

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Deep Learning: Practical Neural Networks with Java

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