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Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow / Sebastian Raschka, Vahid Mirajalili.

By: Raschka, Sebastian.
Contributor(s): Mirajalili, Vahid.
Material type: materialTypeLabelBookCall no.: QA76.73.P98 R373 2018Publication: Birmingham, UK : Packt Publishing, 2018Edition: 2nd ed.Description: xviii, 595 p. : ill.Notes: Reprint. Originally published: 2017.; Includes index.ISBN: 9781787125933; 1787125939.Subject(s): Python (Computer program language) | Machine learning
Contents:
1. Giving computers the ability to learn from data -- 2. Training simple machine learning algorithms for classification -- 3. A tour of machine learning classifiers using scikit-learn -- 4. Building good training sets-data preprocessing -- 5. Compressing data via dimensionality reduction -- 6. Learning best practices for model evaluation and hyperpaarmeter 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 network 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.
List(s) this item appears in: TUPUEY-New Book-201902-02 (foreign)
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Book Book Puey Ungphakorn Library, Rangsit Campus
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General Books QA76.73.P98 R373 2018 (See Similar Items) Checked out 02/05/2019 31379015657167
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Reprint. Originally published: 2017.

Includes index.

1. Giving computers the ability to learn from data -- 2. Training simple machine learning algorithms for classification -- 3. A tour of machine learning classifiers using scikit-learn -- 4. Building good training sets-data preprocessing -- 5. Compressing data via dimensionality reduction -- 6. Learning best practices for model evaluation and hyperpaarmeter 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 network 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.

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