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 real-world applications
This step-by-step guide will help you solve real-world 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 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:
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 real-world 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. Self-Organizing Maps
- Neural networks unsupervised learning
- Unsupervised learning algorithms
-
Kohonen self-organizing maps
- Extending the neural network code to Kohonen
- Zero-dimensional SOM
- One-dimensional SOM
- Two-dimensional 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: Self-Organizing 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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