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

This ebook is included in a Mapt subscription
Yusuke Sugomori et al.

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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 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
Chapter 9: 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 10: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 11: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 12: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 13: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 14: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 15: 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 16: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 17: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 18: 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 19: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
Chapter 20: 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 21: 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 22: 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 23: Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
Chapter 24: Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
Chapter 25: Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 26: Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
Chapter 27: Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
Chapter 28: Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
Chapter 29: Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary

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 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
Chapter 9: 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 10: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 11: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 12: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 13: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 14: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 15: 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 16: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 17: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 18: 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 19: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
Chapter 20: 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 21: 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 22: 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 23: Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
Chapter 24: Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
Chapter 25: Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 26: Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
Chapter 27: Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
Chapter 28: Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
Chapter 29: Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary

Book Details

ISBN 139781788470315
Paperback744 pages
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