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Learn Keras for Deep Neural Networks - A Fast-Track Approach to Modern Deep Learning with Python (Paperback, 1st ed.)
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Learn Keras for Deep Neural Networks - A Fast-Track Approach to Modern Deep Learning with Python (Paperback, 1st ed.)
Expected to ship within 10 - 15 working days
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Learn, understand, and implement deep neural networks in a math-
and programming-friendly approach using Keras and Python. The book
focuses on an end-to-end approach to developing supervised learning
algorithms in regression and classification with practical
business-centric use-cases implemented in Keras. The overall book
comprises three sections with two chapters in each section. The
first section prepares you with all the necessary basics to get
started in deep learning. Chapter 1 introduces you to the world of
deep learning and its difference from machine learning, the choices
of frameworks for deep learning, and the Keras ecosystem. You will
cover a real-life business problem that can be solved by supervised
learning algorithms with deep neural networks. You'll tackle one
use case for regression and another for classification leveraging
popular Kaggle datasets. Later, you will see an interesting and
challenging part of deep learning: hyperparameter tuning; helping
you further improve your models when building robust deep learning
applications. Finally, you'll further hone your skills in deep
learning and cover areas of active development and research in deep
learning. At the end of Learn Keras for Deep Neural Networks, you
will have a thorough understanding of deep learning principles and
have practical hands-on experience in developing enterprise-grade
deep learning solutions in Keras. What You'll Learn Master
fast-paced practical deep learning concepts with math- and
programming-friendly abstractions. Design, develop, train,
validate, and deploy deep neural networks using the Keras framework
Use best practices for debugging and validating deep learning
models Deploy and integrate deep learning as a service into a
larger software service or product Extend deep learning principles
into other popular frameworks Who This Book Is For Software
engineers and data engineers with basic programming skills in any
language and who are keen on exploring deep learning for a career
move or an enterprise project.
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