Plython machine learning /
Sebastian Raschka, Vahid Mirgalili
- 2a. ed.; Revisado y actualizado
- USA : Packt Publishing; 2017
- 595 p. il., graf.; bl. y n. 23 cm.
Texto en inglés
1. Giving computers the ability to learn from data -- 2. Training simple machine learning algoritms for classification -- 3. A tour of machine learning classifiers -- 4. Building goog training sets- data preprocessing -- 5. Compressing data via dimensinality reduction -- 6. Learning best practices for model evaluation and hyperparameter tuning -- 7. Combining different models for ensemble learning -- 8. Applying machine learning to sentiment analysis -- 9. Embedding a machine learning model into a web application -- 10. Predicting continuous target variables with regression analysis -- 11. Working with unlabeled data- clustering analysis -- 12. Implementing a multilayer artificial neural nerwork from scratch -- 13. Parallelizing neural network training with tensorflow -- 14. Going deeper - the mechanics of tensorflow -- 15. Classifying images with deep convolutional neural networks -- 16. Modeling sequential data using recurrent neural networks -- Index