Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems / Aurélien Géron
Tipo de material: TextoDetalles de publicación: Sebastopol : O'Reilly, 2019 Edición: 2nd ed.Descripción: XXV, 819 p. : il. ; 25 cmISBN: 9781492032649Tema(s): Aprendizaje automático (Inteligencia artificial) | Inteligencia artificial | BioinformáticaResumen: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks-scikit-learn, Keras, and TensorFlow-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.Tipo de ítem | Biblioteca de origen | Signatura | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
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Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 681.3/GER/han (Navegar estantería(Abre debajo)) | Prestado | 16/05/2024 | 374461217X |
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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks-scikit-learn, Keras, and TensorFlow-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
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