Picture 1 of 11
Gallery
Picture 1 of 11
Have one to sell?
Deep Learning : A Practitioner's Approach by Gibson And Patterson
US $3.99
ApproximatelyS$ 5.09
or Best Offer
Condition:
“Please see pics for details.”
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
Shipping:
US $5.72 (approx S$ 7.30) USPS Media MailTM.
Located in: Oklahoma City, Oklahoma, United States
Save on combined shipping
Delivery:
Estimated between Fri, 19 Sep and Tue, 23 Sep to 94104
Returns:
60 days return. Seller pays for return shipping.
Coverage:
Read item description or contact seller for details. See all detailsSee all details on coverage
(Not eligible for eBay purchase protection programmes)
Seller assumes all responsibility for this listing.
eBay item number:267244220623
Item specifics
- Condition
- Very Good
- Seller Notes
- “Please see pics for details.”
- ISBN
- 9781491914250
About this product
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
Item description from the seller
Seller feedback (663)
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- n***n (2611)- Feedback left by buyer.Past monthVerified purchaseQuality like new dvd set. Fast service. Secure packing. A+
Seller feedback (663)
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- n***n (2611)- Feedback left by buyer.Past monthVerified purchaseQuality like new dvd set. Fast service. Secure packing. A+