Machine learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python

By: Raschka, SebastianContributor(s): Liu, Yuxi | Mirjalili, VahidMaterial type: TextTextPublication details: Birmingham : Packt Publishing, 2022Description: xxix, 741 pages : illustrations, graphs, charts ; 26 cmISBN: 9781801819312; 1801819319Subject(s): Machine learning | Machine learning Computer programs | Python (Computer program language)DDC classification: 005.133
Contents:
Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering Analysis(N.B. Please use the Look Inside option to see further chapters)
Summary: Fully updated with PyTorch and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks
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Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering Analysis(N.B. Please use the Look Inside option to see further chapters)

Fully updated with PyTorch and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks

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