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...
| Autor principal: | |
|---|---|
| Otros Autores: | , , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2017.
|
| Colección: | EBSCO Academic eBook Collection Complete.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b39297949*spi |
Tabla de Contenidos:
- 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.
