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Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Deep learning overview
- Algorithms for machine learning : preparing for deep learning
- Deep belief nets and stacked denoising autoencoders
- Dropout and convolutional neural networks
- Exploring Java deep learning libraries : DL4J, ND4J, and more
- Approaches to practical applications : recurrent neural networks and more
- Other important deep learning libraries
- What's next?
- Applied machine learning quick start
- Java libraries and platforms for machine learning
- Basic algorithms : classification, regression, and clustering
- Customer relationship prediction with ensembles
- Affinity analysis
- Recommendation engine with Apache Mahout
- Fraud and anomaly detection
- Image recognition with Deeplearning4j
- Activity recognition with mobile phone sensors
- Text mining with mallet : topic modeling and spam detection
- What is next?
- Getting started with neural networks
- Getting neural networks to learn
- Perceptrons and supervised learning
- Self-organizing maps
- Forecasting weather
- Classifying disease diagnosis
- Clustering customer profiles
- Text recognition
- Optimizing and adapting neural networks
- Current trends in neural networks.