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Python Deep Learning

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Description

Books : Computers and Technology : English


Description
This book is for Data Science practitioners as well as
aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with
Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.
What
You Will Learn:

– Get a practical deep dive into deep learning algorithms
– Explore deep
learning further with Theano, Caffe, Keras, and TensorFlow
– Learn about two of the most powerful techniques
at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
– Dive into Deep Belief Nets and Deep Neural Networks
– Discover more deep learning algorithms with
Dropout and Convolutional Neural Networks
– Get to know device strategies so you can use deep learning
algorithms and libraries in the real world

With an increasing interest in AI around the world, deep learning
has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different
industries.

The book will give you all the practical information available on the subject, including the best
practices, using real-world use cases. You will learn to recognize and extract information to increase predictive
accuracy and optimize results.

Starting with a quick recap of important machine learning concepts, the book
will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest
open source libraries such as Theano, Keras, Google’s TensorFlow, and H20. Use this guide to uncover the
difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and
techniques.

Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of
this powerful technology, you’ll find everything inside.

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