Plython machine learning / Sebastian Raschka, Vahid Mirgalili
Material type: TextLanguage: ing Publication details: USA : Packt Publishing; 2017Edition: 2a. ed.; Revisado y actualizadoDescription: 595 p. il., graf.; bl. y n. 23 cmISBN:- 9781787125933
- 21 005.133 R223 2018
Item type | Current library | Collection | Call number | Materials specified | Copy number | Status | Date due | Barcode | |
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Libros | Biblioteca Alba Lucia Corredor Gómez (Medellín) General Stacks | General | 005.133 R223 2018 (Browse shelf(Opens below)) | Ej. 1 | Available | 503756011 | |||
Libros | Biblioteca Alba Lucia Corredor Gómez (Medellín) General Stacks | General | 005.133 R223 2018 (Browse shelf(Opens below)) | Ej. 2 | Available | 503756047 |
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
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