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Deep Learning: Der Ansatz eines Praktizierenden von Patterson, Josh; Gibson, Adam-
by Patterson, Josh; Gibson, Adam | PB | Good
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Ca.EUR 5,76
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eBay-Artikelnr.:197667598225
Artikelmerkmale
- Artikelzustand
- Gut
- Hinweise des Verkäufers
- Binding
- Paperback
- Book Title
- Deep Learning
- Weight
- 1 lbs
- Product Group
- Book
- IsTextBook
- No
- ISBN
- 9781491914250
Über dieses Produkt
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1491914254
ISBN-13
9781491914250
eBay Product ID (ePID)
209763251
Product Key Features
Number of Pages
536 Pages
Language
English
Publication Name
Deep Learning : a Practitioner's Approach
Publication Year
2017
Subject
Data Modeling & Design, Data Processing, Databases / Data Mining
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
1.1 in
Item Weight
32 Oz
Item Length
9.4 in
Item Width
7.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2017-277169
Illustrated
Yes
Synopsis
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning'??especially deep neural networks'??make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'??ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J'??s workflow tool Learn how to use DL4J natively on Spark and Hadoop, Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J's workflow tool Learn how to use DL4J natively on Spark and Hadoop
LC Classification Number
Q325.5
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