TY  - BOOK
AU  - Raschka,Sebastian
AU  - Mirjalili,Mahid
TI  - Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 
T2  - Expert insight
U1  - 005.133 22
PY  - 2019///
CY  - Birmingham, UK
PB  - Packt Publishing
KW  - Machine learning
KW  - BUEsh
KW  - Python (Computer program language)
KW  - COMSCI
KW  - March2020
KW  - Reading book
N1  - 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
N2  - 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
ER  -