Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning: Principles and Implementations

Deep Learning: Principles and Implementations

Kuang, Weidong

Hardcover

General Computers

Currently unavailable to order

ISBN10: 1394256000
ISBN13: 9781394256006
Publisher: Wiley
Published: May 18 2026
Pages: 752
Weight: 3.26
Height: 2.01 Width: 7.11 Depth: 10.11
Language: English

A hands-on and intuitive guide to the foundations of modern deep learning

In Deep Learning: Principles and Implementations, distinguished researcher and professor Weidong Will Kuang delivers an up-to-date exploration of how major deep learning algorithms and architectures are formalized and developed from mathematical equations. The book bridges theory and practice and covers a wide range of fundamental topics, including linear regression, logistic regression, basic neural networks, convolution neural networks, as well as other basic and advanced subjects in the field.

The author provides intuitive introductions to each subject and presents the development of algorithms and architectures from basic mathematical concepts. Along the way, he relies on straightforward math to keep the topics accessible for non-mathematicians and accompanies his explanations with tested Python sample code you can apply in your own work.

You'll also find:

  • Thorough introductions to both linear and logistic regression, offering a solid foundation and insight into neural networks
  • Comprehensive explorations of neural networks, computer vision, natural language processing, generative models, and reinforcement learning
  • Practical exercises that students and practitioners can use to apply and develop the concepts found in the book
  • Balanced treatments of the mathematics, algorithms, architecture, and code that serve as the foundations of a complete understanding of deep learning

Perfect for undergraduate and graduate students with an interest in deep learning, Deep Learning: Principles and Implementations will also benefit practicing software engineers, faculty, and researchers whose work involves deep learning and related topics.

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