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.
