Request Inspection Copy

If you are an Academic or Teacher and wish to consider this book as a prescribed textbook for your course, you may be eligible for a complimentary inspection copy. Please complete this form, including information about your position, campus and course, before adding to cart.

* Required Fields

To complete your Inspection Copy Request you will need to click the Checkout button in the right margin and complete the checkout formalities. You can include Inspection Copies and purchased items in the same shopping cart, see our Inspection Copy terms for further information.

Any Questions? Please email our text Support Team on text@footprint.com.au

Submit

Email this to a friend

* ALL required Fields

Order Inspection Copy

An inspection copy has been added to your shopping cart

Deep Learning

by John D. Kelleher
Pub Date:
08/2019
ISBN:
9780262537551
Format:
Pbk 296 pages
Price:
AU$39.99 NZ$43.47
Product Status: In Stock Now
add to your cart
Instructors
& Academics:
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.
John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is the coauthor of Data Science (also in the MIT Press Essential Knowledge series) and Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press).