Deep Learning.:
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 Quic...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Collecting data from a mobile phone. |
| Beschreibung: | 1 online resource (744 pages) |
| ISBN: | 9781788471718 1788471717 |
Internformat
MARC
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| 300 | |a 1 online resource (744 pages) | ||
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a Collecting data from a mobile phone. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
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| 700 | 1 | |a Kaluza, Bostjan. | |
| 700 | 1 | |a Soares, Fabio M. | |
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| 776 | 0 | 8 | |i Print version: |a Sugomori, Yusuke. |t Deep Learning: Practical Neural Networks with Java. |d Birmingham : Packt Publishing, ©2017 |
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| _version_ | 1816882392622694400 |
| adam_text | |
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| author | Sugomori, Yusuke |
| author2 | Kaluza, Bostjan Soares, Fabio M. Souza, Alan M. F. |
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| author_facet | Sugomori, Yusuke Kaluza, Bostjan Soares, Fabio M. Souza, Alan M. F. |
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| callnumber-raw | QA76.73.J38 .D447 2017 |
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| 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. 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. 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. 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. |
| ctrlnum | (OCoLC)990480792 |
| dewey-full | 006.31 |
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| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn990480792 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:27:53Z |
| institution | BVB |
| isbn | 9781788471718 1788471717 |
| language | English |
| oclc_num | 990480792 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (744 pages) |
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| publishDate | 2017 |
| publishDateSearch | 2017 |
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| publisher | Packt Publishing, |
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| spelling | Sugomori, Yusuke. Deep Learning. Birmingham : Packt Publishing, 2017. 1 online resource (744 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 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. 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 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. 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. Collecting data from a mobile phone. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Apprentissage automatique. Java (Langage de programmation) COMPUTERS General. bisacsh Java (Computer program language) fast Machine learning fast Kaluza, Bostjan. Soares, Fabio M. Souza, Alan M. F. Print version: Sugomori, Yusuke. Deep Learning: Practical Neural Networks with Java. Birmingham : Packt Publishing, ©2017 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1532297 Volltext |
| spellingShingle | Sugomori, Yusuke Deep Learning. 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 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. 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. 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Apprentissage automatique. Java (Langage de programmation) COMPUTERS General. bisacsh Java (Computer program language) fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh95008574 |
| title | Deep Learning. |
| title_auth | Deep Learning. |
| title_exact_search | Deep Learning. |
| title_full | Deep Learning. |
| title_fullStr | Deep Learning. |
| title_full_unstemmed | Deep Learning. |
| title_short | Deep Learning. |
| title_sort | deep learning |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Apprentissage automatique. Java (Langage de programmation) COMPUTERS General. bisacsh Java (Computer program language) fast Machine learning fast |
| topic_facet | Machine learning. Java (Computer program language) Apprentissage automatique. Java (Langage de programmation) COMPUTERS General. Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1532297 |
| work_keys_str_mv | AT sugomoriyusuke deeplearning AT kaluzabostjan deeplearning AT soaresfabiom deeplearning AT souzaalanmf deeplearning |