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Hands-on machine learning with Scikit-Learn and TensorFlow concepts, tools, and techniques to build intelligent systems Aurélien Géron

Por: Tipo de material: TextoTextoCalifornia : O'Reilly, 2017Descripción: xx, 549 páginas : ilustraciones, gráficos ; 24 cmTipo de contenido:
  • texto
Tipo de medio:
  • no mediado
Tipo de soporte:
  • volumen
ISBN:
  • 9781491962299
Tema(s): Clasificación CDD:
  • 006.3 G876h 2017
Contenidos parciales:
The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction -- Neural networks and deep learning. Up and running with TensorFlow ; Introduction to artificial neural networks ; Training deep neural nets ; Distributing TensorFlow across devices and servers ; Convolutional neural networks ; Recurrent neural networks ; Autoencoders ; Reinforcement learning -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures.
Resumen: 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. This practical book shows you how.
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Tipo de ítem Biblioteca actual Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Libro Miguel de Cervantes 006.3 G876h 2017 (Navegar estantería(Abre debajo)) Ej.1 Disponible 00015688

The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction -- Neural networks and deep learning. Up and running with TensorFlow ; Introduction to artificial neural networks ; Training deep neural nets ; Distributing TensorFlow across devices and servers ; Convolutional neural networks ; Recurrent neural networks ; Autoencoders ; Reinforcement learning -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures.

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. This practical book shows you how.

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