TY  - BOOK
AU  - Sugomori,Yusuke
AU  - Kaluza,Bostjan
AU  - Soares,Fabio M.
AU  - Souza,Alan M.F.
TI  - Deep Learning
SN  - 9781788471718
AV  - QA76.73.J38 .D447 2017
U1  - 006.31 23
PY  - 2017///
CY  - Birmingham
PB  - Packt Publishing
KW  - Machine learning
KW  - Java (Computer program language)
KW  - Apprentissage automatique
KW  - Java (Langage de programmation)
KW  - COMPUTERS
KW  - General
KW  - bisacsh
KW  - fast
KW  - Electronic books
N1  - Collecting data from a mobile phone; 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
N2  - 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
UR  - https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1532297
ER  -