| Description |
1 online resource (744 pages) |
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text file |
| Contents |
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. |
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Chapter 4: Dropout and Convolutional Neural NetworksDeep 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. |
| Contents |
Machine learning librariesBuilding 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. |
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Chapter 6: Recommendation Engine with Apache MahoutBasic 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. |
| Note |
Collecting data from a mobile phone. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
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Java (Computer program language)
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Java (Computer program language) |
| Genre/Form |
Electronic books.
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| Added Author |
Kaluza, Bostjan.
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Soares, Fabio M.
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Souza, Alan M. F.
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| Other Form: |
Print version: Sugomori, Yusuke. Deep Learning: Practical Neural Networks with Java. Birmingham : Packt Publishing, ©2017 |
| ISBN |
9781788471718 (electronic book) |
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1788471717 (electronic book) |
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