Deep learning a practitioner's approach
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical informati...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing, [China] :
O'Reilly Media
2017.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630455906719 |
Tabla de Contenidos:
- A review of machine learning
- Foundations of neural networks and deep learning
- Fundamentals of deep networks
- Major architecture of deep networks
- Building deep networks
- Tuning deep networks
- Tuning specific deep network architectures
- Vectorization
- Using deep learning and DL4J on Spark
- What is artificial intelligence?
- RL4J and reinforcement learning
- Numbers everyone should know
- Neural networks and backpropagation: a mathematical approach
- Using the ND4J API
- Using DataVec
- Working with DL4J from source
- Setting up DL4J projects
- Setting up GPUs for DL4J projects
- Troubleshooting DL4J installations.
