If you’re a designer, developer, entrepreneur, student, educator, business leader, artist, or simply curious about AR’s possibilities, this insightful guide explains how you can become involved with an exciting, fast-moving technology.
You’ll explore how:
Dr. Helen Papagiannis is recognized as a world leading expert in the field of Augmented Reality (AR). She has been working with AR for a decade as a researcher, designer, and technology evangelist with a focus on storytelling and creating compelling experiences in AR. Dr. Papagiannis was named among the NEXT 100 Top Influencers (#16) of the Digital Media Industry in 2013, and is featured as an innovator in the book, “Augmented Reality: An Emerging Technologies Guide to AR”, published in 2013. Her work and research in the field include her past roles as Chief Innovation Officer at Infinity Augmented Reality Inc. (New York City and Tel Aviv), and Senior Research Associate at York University’s Augmented Reality Lab in the Department of Film, Faculty of Fine Art (Toronto).
Dr. Papagiannis has presented her interactive work and Ph.D. research at global conferences and invited events including TEDx (Technology, Entertainment, Design), ISMAR (International Society for Mixed and Augmented Reality) and ISEA (International Symposium for Electronic Art). Her TEDx 2011 talk was featured among the Top 10 Talks on Augmented Reality and Gamified Life. Prior to her augmented life, Dr. Papagiannis was a member of the internationally renowned Bruce Mau Design studio where she was project lead on “Massive Change: The Future of Global Design”, an internationally touring exhibition and best-selling book examining the new inventions, technologies, and events changing the world.
It would have been easy for id to coast on their success, but instead they made the audacious decision to throw away everything they had built and start from scratch. Game Engine Black Book: Doom is the story of how they did it.
This is a book about history and engineering. Don’t expect much prose (the author’s English has improved since the first book but is still broken). Instead you will find inside extensive descriptions and drawings to better understand all the challenges id Software had to overcome.
From the hardware -- the Intel 486 CPU, the Motorola 68040 CPU, and the NeXT workstations -- to the game engine’s revolutionary design, open up to learn how DOOM changed the gaming industry and became a legend among video games.
The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details