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Andrew Glassners Notebook
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Book Synopsis Andrew Glassner's Notebook by : Andrew S. Glassner
Download or read book Andrew Glassner's Notebook written by Andrew S. Glassner and published by Morgan Kaufmann. This book was released on 1999 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning written by Andrew Glassner and published by No Starch Press. This book was released on 2021-06-22 with total page 1239 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Andrew Glassner's Other Notebook by : Andrew Glassner
Download or read book Andrew Glassner's Other Notebook written by Andrew Glassner and published by A K Peters/CRC Press. This book was released on 2002-07-15 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Morphs, Mallards, and Montages by : Andrew Glassner
Download or read book Morphs, Mallards, and Montages written by Andrew Glassner and published by CRC Press. This book was released on 2021-03-30 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning for the Life Sciences by : Bharath Ramsundar
Download or read book Deep Learning for the Life Sciences written by Bharath Ramsundar and published by O'Reilly Media. This book was released on 2019-04-10 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Practical Deep Learning by : Ronald T. Kneusel
Download or read book Practical Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-02-23 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Generative Deep Learning by : David Foster
Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Real Time Cameras by : Mark Haigh-Hutchinson
Download or read book Real Time Cameras written by Mark Haigh-Hutchinson and published by CRC Press. This book was released on 2009-04-02 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Programming Interactivity by : Joshua Noble
Download or read book Programming Interactivity written by Joshua Noble and published by "O'Reilly Media, Inc.". This book was released on 2009-07-21 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make cool stuff. If you're a designer or artist without a lot of programming experience, this book will teach you to work with 2D and 3D graphics, sound, physical interaction, and electronic circuitry to create all sorts of interesting and compelling experiences -- online and off. Programming Interactivity explains programming and electrical engineering basics, and introduces three freely available tools created specifically for artists and designers: Processing, a Java-based programming language and environment for building projects on the desktop, Web, or mobile phones Arduino, a system that integrates a microcomputer prototyping board, IDE, and programming language for creating your own hardware and controls OpenFrameworks, a coding framework simplified for designers and artists, using the powerful C++ programming language BTW, you don't have to wait until you finish the book to actually make something. You'll get working code samples you can use right away, along with the background and technical information you need to design, program, build, and troubleshoot your own projects. The cutting edge design techniques and discussions with leading artists and designers will give you the tools and inspiration to let your imagination take flight.
Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Processing for Visual Artists by : Andrew Glassner
Download or read book Processing for Visual Artists written by Andrew Glassner and published by CRC Press. This book was released on 2011-09-27 with total page 955 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Deep Learning from the Basics by : Koki Saitoh
Download or read book Deep Learning from the Basics written by Koki Saitoh and published by Packt Publishing Ltd. This book was released on 2021-03-08 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering the technologies required for image recognitionBook Description Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning. What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is for Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
Book Synopsis Jim Blinn's Corner: Notation, Notation, Notation by : Jim Blinn
Download or read book Jim Blinn's Corner: Notation, Notation, Notation written by Jim Blinn and published by Elsevier. This book was released on 2002-07-16 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third entry in the Jim Blinn's Corner series, this is, like the others, a handy compilation of selected installments of his influential column. But here, for the first time, you get the "Director's Cut" of the articles: revised, expanded, and enhanced versions of the originals. What's changed? Improved mathematical notation, more diagrams, new solutions. What remains the same? All the things you've come to rely on: straight answers, irreverent style, and innovative thinking. This is Jim Blinn at his best - now even better. Features 21 expanded and updated installments of "Jim Blinn's Corner," dating from 1995 to 2001, and never before published in book form Includes "deleted scenes"—tangential explorations that didn't make it into the original columns Details how Blinn represented planets in his famous JPL flyby animations Explores a wide variety of other topics, from the concrete to the theoretical: assembly language optimization for parallel processors, exotic usage of C++ template instantiation, algebraic geometry, a graphical notation for tensor contraction, and his hopes for a future world
Book Synopsis Salsa Dancing into the Social Sciences by : Kristin Luker
Download or read book Salsa Dancing into the Social Sciences written by Kristin Luker and published by Harvard University Press. This book was released on 2010-04-10 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: “You might think that dancing doesn’t have a lot to do with social research, and doing social research is probably why you picked this book up in the first place. But trust me. Salsa dancing is a practice as well as a metaphor for a kind of research that will make your life easier and better.” Savvy, witty, and sensible, this unique book is both a handbook for defining and completing a research project, and an astute introduction to the neglected history and changeable philosophy of modern social science. In this volume, Kristin Luker guides novice researchers in: knowing the difference between an area of interest and a research topic; defining the relevant parts of a potentially infinite research literature; mastering sampling, operationalization, and generalization; understanding which research methods best answer your questions; beating writer’s block. Most important, she shows how friendships, non-academic interests, and even salsa dancing can make for a better researcher. “You know about setting the kitchen timer and writing for only an hour, or only 15 minutes if you are feeling particularly anxious. I wrote a fairly large part of this book feeling exactly like that. If I can write an entire book 15 minutes at a time, so can you.”
Download or read book Graphics Gems II written by James Arvo and published by Elsevier. This book was released on 2013-10-22 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphics Gems II is a collection of articles shared by a diverse group of people that reflect ideas and approaches in graphics programming which can benefit other computer graphics programmers. This volume presents techniques for doing well-known graphics operations faster or easier. The book contains chapters devoted to topics on two-dimensional and three-dimensional geometry and algorithms, image processing, frame buffer techniques, and ray tracing techniques. The radiosity approach, matrix techniques, and numerical and programming techniques are likewise discussed. Graphics artists and computer programmers will find the book invaluable.
Book Synopsis Learning Deep Learning by : Magnus Ekman
Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1105 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Book Synopsis Deep Learning from Scratch by : Seth Weidman
Download or read book Deep Learning from Scratch written by Seth Weidman and published by O'Reilly Media. This book was released on 2019-09-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework
