Tag: Learning

Price: $99.99 – $66.22
(as of Nov 21,2024 11:06:39 UTC – Details)

From the Publisher

‘Best Yet’

“Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet.”

—Peter Shirley, Distinguished Research Engineer, Nvidia

‘Read It Cover-to-Cover’

“I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding.“

—Richard Szeliski, author of Computer Vision: Algorithms and Applications

‘Great Introduction to Deep Learning’

“This book is a great introduction to machine learning, in general, and more specifically to deep learning (neural networks). The author thoroughly explains each concept using pictures instead of math.”

—Mike, Amazon reviewer

About The Author

Dr. Andrew Glassner is a Senior Research Scientist at Weta Digital, where he uses deep learning to help artists produce visual effects for film and television. He was Technical Papers Chair for SIGGRAPH ’94, Founding Editor of the Journal of Computer Graphics Tools, and Editor-in-Chief of ACM Transactions on Graphics. His prior books include the Graphics Gems series and the textbook Principles of Digital Image Synthesis. Glassner holds a PhD from UNC-Chapel Hill. He paints, plays jazz piano, and writes novels. He can be followed on Twitter as @AndrewGlassner.

Who Should Read This Book

You don’t need math or programming experience. You don’t need to be a computer whiz. You don’t have to be a technologist at all!

This book is for anyone with curiosity and a desire to look behind the headlines. You may be surprised that most of the algorithms of deep learning aren’t very complicated or hard to understand. They’re usually simple and elegant and gain their power by being repeated millions of times over huge databases.

In addition to satisfying pure intellectual curiosity, Glassner wrote this book for people who come face to face with deep learning, either in their own work or when interacting with others who use it. After all, one of the best reasons to understand AI is so we can use it ourselves! We can build AI systems now that help us do our work better, enjoy our hobbies more deeply, and understand the world around us more fully.

If you want to know how this stuff works, you’re going to feel right at home.

About the Publisher

No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.

Publisher ‏ : ‎ No Starch Press; Illustrated edition (June 29, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 768 pages
ISBN-10 ‏ : ‎ 1718500726
ISBN-13 ‏ : ‎ 978-1718500723
Item Weight ‏ : ‎ 3.7 pounds
Dimensions ‏ : ‎ 7 x 1.56 x 9.25 inches

Deep learning is a powerful subset of machine learning that mimics the way the human brain works to process data and make decisions. It has revolutionized many industries, from healthcare to finance to autonomous vehicles, by enabling computers to learn from large amounts of data and make accurate predictions.

One way to understand deep learning is through visualization. Visualizing complex algorithms and neural networks can help us grasp the inner workings of these models and appreciate their capabilities. In this post, we will explore deep learning through a visual approach, breaking down key concepts and techniques into easily digestible graphics and diagrams.

We will start by explaining the basics of neural networks, the building blocks of deep learning. We will then delve into convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two popular architectures used in image and text processing, respectively. Through visual examples, we will demonstrate how these networks process data and extract features to make accurate predictions.

We will also discuss the importance of data visualization in deep learning, showcasing tools and techniques for visualizing data, model performance, and decision-making processes. By visualizing the results of deep learning models, we can gain valuable insights and interpret their predictions with more confidence.

In conclusion, deep learning is a complex and powerful technology that can be better understood through visual representation. By visualizing neural networks, data, and model performance, we can unlock the full potential of deep learning and harness its capabilities to solve real-world problems. Join us on this visual journey through the world of deep learning and discover the beauty of AI in action.
#Deep #Learning #Visual #Approach