Deep training in terms of practice
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All that the practitioner developer should know in order to start using deep learning to solve real problems!
Interest in machine learning goes off scale, but high expectations often destroy projects at an early stage. How is machine learning - and especially deep neural networks - can change your organization?
This book not only contains practically useful information about the subject, but also helps to start creating effective deep learning networks.
The authors first reveal the fundamental issues of deep learning - setting, parallelization, vectorization, conveyors of operations - relevant for any library, and then move to the Deeplearning4J (DL4J) library, designed to develop technological processes of a professional level.
With real examples, the reader will get acquainted with the methods and strategies of teaching deep networks with various architecture and their parallelization in Hadoop and Spark clusters.
The concepts of machine learning in general and deep learning in particular
the evolution of deep networks from neural
the main architectures of deep networks, including sparkle and recurrent neural networks
how to choose a network that meets The task set
The basics of setting neural networks in general and specific deep architectures
Application of vectorization methods to data from various types
Interest in machine learning goes off scale, but high expectations often destroy projects at an early stage. How is machine learning - and especially deep neural networks - can change your organization?
This book not only contains practically useful information about the subject, but also helps to start creating effective deep learning networks.
The authors first reveal the fundamental issues of deep learning - setting, parallelization, vectorization, conveyors of operations - relevant for any library, and then move to the Deeplearning4J (DL4J) library, designed to develop technological processes of a professional level.
With real examples, the reader will get acquainted with the methods and strategies of teaching deep networks with various architecture and their parallelization in Hadoop and Spark clusters.
The concepts of machine learning in general and deep learning in particular
the evolution of deep networks from neural
the main architectures of deep networks, including sparkle and recurrent neural networks
how to choose a network that meets The task set
The basics of setting neural networks in general and specific deep architectures
Application of vectorization methods to data from various types
Author:
Author:Gibson Adam, Patterson Josh
Cover:
Cover:Hard
Category:
- Category:Computer & Technology
ISBN:
ISBN:978-5-97060-481-6
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