Andrew Glassner's Notebook

1999
Andrew Glassner's Notebook

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  • Author: Andrew S. Glassner
  • Publisher: Morgan Kaufmann
  • ISBN: 9781558605985
  • Category : Computers
  • Languages : en
  • Pages : 336

This is a collection of 18 columns written by Andrew Glassner for Computer Graphic and Applications magazine. As well as the published material, the book includes notes and corrections to the original articles, a chapter of introduction, and additional text and graphics not originally included. Topics range from computer graphics and art, to the ethics of computers in society.

Deep Learning

2021-06-22
Deep Learning

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  • Author: Andrew Glassner
  • Publisher: No Starch Press
  • ISBN: 1718500734
  • Category : Computers
  • Languages : en
  • Pages : 1239

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations

Andrew Glassner's Other Notebook

2002-07-15
Andrew Glassner's Other Notebook

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  • Author: Andrew Glassner
  • Publisher: A K Peters/CRC Press
  • ISBN:
  • Category : Computers
  • Languages : en
  • Pages : 280

Once again, Andrew Glassner opens his notebook and invites readers in a wide range of stimulating explorations of art, nature, and computer graphics. The text of this book is accessible and informal, and richly illustrated with hundreds of images illustrating topics from Celtic knotwork and lightning to soap bubbles and the mysteries of camera shutters. This well-known author brings decades of experience as a researcher and writer to create discussions that are as clear as they are captivating. The book is intended for hobbyists, researchers, students, and anyone interested in computer graphics and its uses for creative exploration.

Morphs, Mallards, and Montages

2021-03-30
Morphs, Mallards, and Montages

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  • Author: Andrew Glassner
  • Publisher: CRC Press
  • ISBN: 1439870071
  • Category : Computers
  • Languages : en
  • Pages : 345

This book is for everyone who's interested in computer graphics and how it can take us on exciting journeys powered by imagination and a love of discovery and invention. Each chapter investigates a unique topic and gives you the tools to continue that exploration on your own. Examine the possibilities of: - Pop-up books and cards - Reconstructing shredded documents - Crop circles - Weaves and Tartans - Morphing images and much more! Browse and enjoy the array of visual ideas or roll up your sleeves and write your own code.

Deep Learning for the Life Sciences

2019-04-10
Deep Learning for the Life Sciences

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  • Author: Bharath Ramsundar
  • Publisher: O'Reilly Media
  • ISBN: 1492039802
  • Category : Science
  • Languages : en
  • Pages : 236

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Practical Deep Learning

2021-02-23
Practical Deep Learning

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  • Author: Ronald T. Kneusel
  • Publisher: No Starch Press
  • ISBN: 1718500742
  • Category : Computers
  • Languages : en
  • Pages : 463

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Generative Deep Learning

2019-06-28
Generative Deep Learning

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  • Author: David Foster
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1492041890
  • Category : Computers
  • Languages : en
  • Pages : 360

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Real Time Cameras

2009-04-02
Real Time Cameras

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  • Author: Mark Haigh-Hutchinson
  • Publisher: CRC Press
  • ISBN: 1000065081
  • Category : Art
  • Languages : en
  • Pages : 544

The control of cameras is as important in games as it is in cinema. How the camera tracks and moves determines our point of view and influences our attitude towards the content. A poorly designed camera system in a game can disrupt a users experience, while a well-designed one can make a good game into a great one. The challenge in games is that th

Processing for Visual Artists

2011-09-27
Processing for Visual Artists

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  • Author: Andrew Glassner
  • Publisher: CRC Press
  • ISBN: 1466509732
  • Category : Computers
  • Languages : en
  • Pages : 955

Learn how to create gorgeous and expressive imagery with the Processing graphics language and environment. It's easy with this practical, hands-on book. Processing is for artists, designers, visualization creators, hobbyists, or anyone else looking to create images, animation, and interactive pieces for art, education, science, or business. Process

Deep Learning

2017-07-28
Deep Learning

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  • Author: Josh Patterson
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1491914211
  • Category : Computers
  • Languages : en
  • Pages : 532

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