Sebastian Raschka - Python Machine Learning: Machine Learning And Deep Learning With Python, Scikit-learn, And Tensorflow, 2nd Edition

Python Machine Learning: Machine Learning And Deep Learning With Python, Scikit-learn, And Tensorflow, 2nd Edition por Sebastian Raschka epub

Descargar Python Machine Learning: Machine Learning And Deep Learning With Python, Scikit-learn, And Tensorflow, 2nd Edition por Sebastian Raschka Epub y PDF

  • Título: Python Machine Learning: Machine Learning And Deep Learning With Python, Scikit-learn, And Tensorflow, 2nd Edition
  • Autores: Sebastian Raschka
  • Publicado: 20 de septiembre de 2017
  • ISBN: 9781787125933
  • Tamaño: .MB
  • Nº de páginas: 622
  • Idiomas: Español
  • Editorial: www.fapermex.mx
  • Valoración: ★★★★★
  • País: España
  • Genero: Novela kindle
  • Comprimido: no
  • Formato : PDF, EPUB

Sebastian Raschka con Python Machine Learning: Machine Learning And Deep Learning With Python, Scikit-learn, And Tensorflow, 2nd Edition

Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsBook DescriptionMachine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.What you will learnUnderstand the key frameworks in data science, machine learning, and deep learningHarness the power of the latest Python open source libraries in machine learningExplore machine learning techniques using challenging real-world dataMaster deep neural network implementation using the TensorFlow libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysisTable 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 Sets - Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisEmbedding a Machine Learning Model into a Web ApplicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data - Clustering AnalysisImplementing a Multilayer Artificial Neural Network from ScratchParallelizing Neural Network Training with TensorFlowGoing Deeper - The Mechanics of TensorFlowClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data using Recurrent Neural Networks
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