Python machine learning: machine learning and deep learning with python, scikit-learn, and tensorflow /
Raschka, Sebastian
Python machine learning: machine learning and deep learning with python, scikit-learn, and tensorflow / - 2nd ed. Fully revised and updated. - Birmingham: Packt, 2017. - xviii, 595 p. : ill. 25 cm.
Includes Index.
Giving computers the ability to learn from data -- Training simple machine learning algorithms -- A tour of machine learning classifiers -- Building good training sets -Data processing -- Compressing data via Dimensional reduction -- Learning best practices for model evaluation and hyperparameter tuning -- Combining different models for ensemble learning -- Applying machine learning to sentiment analysis -- Embedding a machine learning model into a web application -- Predicting continuous target variables with regression analysis -- Working with unlabeled data- clustering analysis -- Implementing a multilayered artificial neural network fron scratch -- Parrallelizing neural network training with tensorflow -- Going deeper - the mechanics of tensorflow -- Classifying images with deep convolutional neural networks -- Modelling sequential data using recurrent neural networks.
9781787125933
QA76.73.P98 / .R37 2017
Python machine learning: machine learning and deep learning with python, scikit-learn, and tensorflow / - 2nd ed. Fully revised and updated. - Birmingham: Packt, 2017. - xviii, 595 p. : ill. 25 cm.
Includes Index.
Giving computers the ability to learn from data -- Training simple machine learning algorithms -- A tour of machine learning classifiers -- Building good training sets -Data processing -- Compressing data via Dimensional reduction -- Learning best practices for model evaluation and hyperparameter tuning -- Combining different models for ensemble learning -- Applying machine learning to sentiment analysis -- Embedding a machine learning model into a web application -- Predicting continuous target variables with regression analysis -- Working with unlabeled data- clustering analysis -- Implementing a multilayered artificial neural network fron scratch -- Parrallelizing neural network training with tensorflow -- Going deeper - the mechanics of tensorflow -- Classifying images with deep convolutional neural networks -- Modelling sequential data using recurrent neural networks.
9781787125933
QA76.73.P98 / .R37 2017