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Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks (Paperback, 1st ed.)
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Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks (Paperback, 1st ed.)
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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.
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