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Deep Learning (The MIT Press Essential Knowledge series) 2019
US $12.90
ApproximatelyRM 54.87
Condition:
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.
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Item specifics
- Condition
- ISBN
- 9780262537551
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262537559
ISBN-13
9780262537551
eBay Product ID (ePID)
5038293491
Product Key Features
Number of Pages
296 Pages
Publication Name
Deep Learning
Language
English
Publication Year
2019
Subject
Intelligence (Ai) & Semantics, Neural Networks, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Computers
Series
The MIT Press Essential Knowledge Ser.
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
9.3 Oz
Item Length
6.9 in
Item Width
5 in
Additional Product Features
Intended Audience
Trade
LCCN
2018-059550
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning--major trends, possible developments, and significant challenges., An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning- gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning-major trends, possible developments, and significant challenges.
LC Classification Number
Q325.5.K454 2019
Item description from the seller
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Seller feedback (95)
- s***o (325)- Feedback left by buyer.Past monthVerified purchaseLoved the buy one get one free, thank you!
- p***t (32)- Feedback left by buyer.Past monthVerified purchaseSuper fast shipping, definitely will buy again. A++++++
- e***x (594)- Feedback left by buyer.Past monthVerified purchaseThank you for a positive transaction and fast shippingReply from: getessentialshub- Feedback replied by seller getessentialshub.- Feedback replied by seller getessentialshub.Thank you so much for your kind words! We're glad you had a smooth experience β shop with us again for more exciting deals and freebies! ππ¦