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Python machine learning : Machine learning and deep learning with python, sckit-learn, and tensorflow / Raschka, Sebastian

Por: Colaborador(es): Tipo de material: TextoTextoDetalles de publicación: Columbia, USA Packt publishing 2017Edición: 2a edDescripción: 721 17 cmISBN:
  • 978-1-78712-593-3
Clasificación CDD:
  • 006.4  R24
Contenidos:
Giving Computers the Ability to Learn from Data -- Training Simple Machine Learning Algorithms for Classification -- A Tour of Machine Learning Classifiers Using scikit-learn -- Building Good Training sets - Data preprocessing -- Compressing Data via Dimensionality 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 -- Implementing a Multilayer Artificial Neural Network from Scratch -- Parallelizing Neural Network Training with TensorFlow -- Going Deeper - The Mechanics of TensorFlow -- Classifying Images with Deep Convolutionnal Neural Networks -- Modeling Seqquential Data Using Recurrent Neural Networks
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Giving Computers the Ability to Learn from Data -- Training Simple Machine Learning Algorithms for Classification -- A Tour of Machine Learning Classifiers Using scikit-learn -- Building Good Training sets - Data preprocessing -- Compressing Data via Dimensionality 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 -- Implementing a Multilayer Artificial Neural Network from Scratch -- Parallelizing Neural Network Training with TensorFlow -- Going Deeper - The Mechanics of TensorFlow -- Classifying Images with Deep Convolutionnal Neural Networks -- Modeling Seqquential Data Using Recurrent Neural Networks

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