Hands-on unsupervised learning using Python how to build applied machine learning solutions from unlabeled data
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly
[2019]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630560106719 |
Tabla de Contenidos:
- Part 1. Fundamentals of unsupervised learning. Unsupervised learning in the machine learning ecosystem
- End-to-end machine learning project
- Part 2. Unsupervised learning using Scikit-learn. Dimensionality reduction
- Anomaly detection
- Clustering
- Group segmentation
- Part 3. Unsupervised learning using TensorFlow and Keras. Autoencoders
- Hands-on autoencoder
- Semisupervised learning
- Part 4. Deep unsupervised learning using TensorFlow and Keras. Recommender systems using restricted Boltzmann machines
- Feature detection using deep belief networks
- Generative adversarial networks
- Time series clustering
- Conclusion.
