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Deep Learning with Python - A Hands-on Introduction (Paperback, 1st ed.)
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Deep Learning with Python - A Hands-on Introduction (Paperback, 1st ed.)
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Discover the practical aspects of implementing deep-learning
solutions using the rich Python ecosystem. This book bridges the
gap between the academic state-of-the-art and the industry
state-of-the-practice by introducing you to deep learning
frameworks such as Keras, Theano, and Caffe. The practicalities of
these frameworks is often acquired by practitioners by reading
source code, manuals, and posting questions on community forums,
which tends to be a slow and a painful process. Deep Learning with
Python allows you to ramp up to such practical know-how in a short
period of time and focus more on the domain, models, and
algorithms. This book briefly covers the mathematical prerequisites
and fundamentals of deep learning, making this book a good starting
point for software developers who want to get started in deep
learning. A brief survey of deep learning architectures is also
included. Deep Learning with Python also introduces you to key
concepts of automatic differentiation and GPU computation which,
while not central to deep learning, are critical when it comes to
conducting large scale experiments. What You Will Learn Leverage
deep learning frameworks in Python namely, Keras, Theano, and Caffe
Gain the fundamentals of deep learning with mathematical
prerequisites Discover the practical considerations of large scale
experiments Take deep learning models to production Who This Book
Is For Software developers who want to try out deep learning as a
practical solution to a particular problem. Software developers in
a data science team who want to take deep learning models developed
by data scientists to production.
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