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Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 / Sebastian Raschka, Vahid Mirjalili.

By: Contributor(s): Material type: TextTextLanguage: English Summary language: English Series: Expert insightPublisher: Birmingham, UK : Packt Publishing, 2019Edition: Third editionDescription: xxi, 741 pages : illustrations (black and white) ; 24 cmContent type:
  • text
  • still image
Media type:
  • computer
Carrier type:
  • online resource
Subject(s): Genre/Form: Additional physical formats: Print version :: No titleDDC classification:
  • 005.133 RAS 22
Contents:
Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple ML Algorithms for ClassificationML Classifiers Using scikit-learnBuilding Good Training Datasets - Data PreprocessingCompressing Data via Dimensionality ReductionBest Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying ML to Sentiment AnalysisEmbedding a ML Model into a Web ApplicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data - Clustering AnalysisImplementing Multilayer Artificial Neural NetworksParallelizing Neural Network Training with TensorFlowTensorFlow MechanicsClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data Using Recurrent Neural NetworksGANs for Synthesizing New DataRL for Decision Making in Complex Environments.
Summary: Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. This new third edition is updated for TensorFlow 2 and the latest additions to
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library Lower Floor Baccah 005.133 RAS (Browse shelf(Opens below)) Checked out 01/10/2024 000049059
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Includes bibliographical references and index.

Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple ML Algorithms for ClassificationML Classifiers Using scikit-learnBuilding Good Training Datasets - Data PreprocessingCompressing Data via Dimensionality ReductionBest Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying ML to Sentiment AnalysisEmbedding a ML Model into a Web ApplicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data - Clustering AnalysisImplementing Multilayer Artificial Neural NetworksParallelizing Neural Network Training with TensorFlowTensorFlow MechanicsClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data Using Recurrent Neural NetworksGANs for Synthesizing New DataRL for Decision Making in Complex Environments.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. This new third edition is updated for TensorFlow 2 and the latest additions to

In English; summary in English.

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