Téléchargez le livre : Applied Deep Learning

Applied Deep Learning

A Case-Based Approach to Understanding Deep Neural Networks

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Éditeur :

Apress


Paru le : 2018-09-07



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Description

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. 
The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. 
Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). 
What You Will Learn

Implement advanced techniques in the right way in Python and TensorFlow
Debug and optimize advanced methods (such as dropout and regularization)
Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
Set up a machine learning project focused on deep learning on a complex dataset


Who This Book Is For

Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming. 
Pages
410 pages
Collection
n.c
Parution
2018-09-07
Marque
Apress
EAN papier
9781484237892
EAN PDF
9781484237908
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
41
Taille du fichier
12974 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
66,05 €
EAN EPUB
9781484237908
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
41
Taille du fichier
17828 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
66,05 €

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