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Python machine learning: machine learning and deep learning with python, scikit-learn, and tensorflow /

By: Contributor(s): Material type: TextTextPublication details: Birmingham: Packt, 2017.Edition: 2nd ed. Fully revised and updatedDescription: xviii, 595 p. : ill. 25 cmISBN:
  • 9781787125933
LOC classification:
  • QA76.73.P98 .R37 2017
Contents:
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.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books Zetech Library - TRC General Stacks Non-fiction QA76.73.P98 .R37 2017 (Browse shelf(Opens below)) C1 Checked out 04/10/2024 Z009618

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.

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