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Deep Learning with Python / François Chollet [Texto impreso]

Original language: English Publisher: Shelter Island (New York, Estados Unidos) : Manning, cop. 2018Description: XXI, 361 p. : il. ; 23 cmISBN: 9781617294433; 1617294438Subject(s): Aprendizaje automático

| Python (lenguaje de programación)

Contents:

PART I. Fundamentals of deep learning ; What is deep learning? ; Before we begin: the mathematical building blocks of neural networks ; Getting started with neural networks ; Fundamentals of machine learning ; PART II. Deep learning in practice ; Deep learning for computer vision ; Deep learning for text and sequences ; Advanced deep-learning best practices ; Generative deep learning.

Abstract: Deep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.Deep Learning with Python is structured around a series of practicalcode examples that illustrate each new concept introduced anddemonstrate best practices. By the time you reach the end of this book,you will have become a Keras expert and will be able to apply deeplearning in your own projects.KEY FEATURES* Practical code examples* In-depth introduction to Keras* Teaches the difference between Deep Learning and AIABOUT THE TECHNOLOGYDeep learning is the technology behind photo tagging systems atFacebook and Google, self-driving cars, speech recognition systems onyour smartphone, and much more.AUTHOR BIOFrancois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io

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Title notes

Item type	Current location	Collection	Call number	Status	Date due	Barcode	Course reserves
Libro	Biblioteca Universidad Europea del Atlántico Fondo General	No ficción	004.85 CHA dee	Available		3666	Inteligencia Artificial

Índice analítico: p. 353-361.

PART I. Fundamentals of deep learning ;
What is deep learning? ;
Before we begin: the mathematical building blocks of neural networks ;
Getting started with neural networks ;
Fundamentals of machine learning ;
PART II. Deep learning in practice ;
Deep learning for computer vision ;
Deep learning for text and sequences ;
Advanced deep-learning best practices ;
Generative deep learning.

Deep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.Deep Learning with Python is structured around a series of practicalcode examples that illustrate each new concept introduced anddemonstrate best practices. By the time you reach the end of this book,you will have become a Keras expert and will be able to apply deeplearning in your own projects.KEY FEATURES* Practical code examples* In-depth introduction to Keras* Teaches the difference between Deep Learning and AIABOUT THE TECHNOLOGYDeep learning is the technology behind photo tagging systems atFacebook and Google, self-driving cars, speech recognition systems onyour smartphone, and much more.AUTHOR BIOFrancois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io