Author: Louis E. Frenzel
Publisher: Sams Technical Publishing
ISBN:
Category : Computers
Languages : en
Pages : 372
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Book Description
Making computers more ueful by making them; Knowledge representation; An approach to problem solving; Introduction to expert systems; Developing an expert system; Natural language processing and voice recognition; Computer vision; Robotics and AI; programming in LISP; Prolog and other AI languages; AI hardware and the future of AI; Appendices; Index.
Author: Louis E. Frenzel
Publisher: Newnes
ISBN: 9780750697095
Category : Technology & Engineering
Languages : en
Pages : 214
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Book Description
Crash Course in Digital Technology teaches the basics of digital electronics theory and circuits in an easy-to-understand format. Each chapter includes learning objectives, clear explanations and examples, and an end-of-chapter self-quiz. The drill-and-review software included with the book allows learners to test themselves on the contents of each chapter, providing a second reinforcement of the material. A final chapter teaches the basics of troubleshooting digital circuits. With the two other Crash Course books, Electronics Technology and Microprocessor Technology, this book forms a complete course in electronics and microcomputer technology appropriate for technical schools, industrial training, and hobbyists. Louis Frenzel is an experienced electronics engineer and educator, as well as the author of many magazine articles and texts. He is currently an instructor at Austin Community College in Austin, Texas. Drill-and-review software included Clear, easy format Self-paced introduction to digital electronics
Author: Hadelin de Ponteves
Publisher: Packt Publishing Ltd
ISBN: 1838645551
Category : Computers
Languages : en
Pages : 361
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Book Description
Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key FeaturesLearn from friendly, plain English explanations and practical activitiesPut ideas into action with 5 hands-on projects that show step-by-step how to build intelligent softwareUse AI to win classic video games and construct a virtual self-driving carBook Description Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learnMaster the basics of AI without any previous experienceBuild fun projects, including a virtual-self-driving car and a robot warehouse workerUse AI to solve real-world business problemsLearn how to code in PythonDiscover the 5 principles of reinforcement learningCreate your own AI toolkitWho this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).
Author: Jeremy Kepner
Publisher: MIT Press
ISBN: 0262347911
Category : Computers
Languages : en
Pages : 443
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Book Description
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.
Author: Pradip K. Chande
Publisher: Allied Publishers
ISBN: 9788170235125
Category : Automation
Languages : en
Pages : 836
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Book Description
Author: Thomas C. Bartee
Publisher: Sams Technical Publishing
ISBN:
Category : Computers
Languages : en
Pages : 332
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Book Description
Author: Efraim Turban
Publisher: Macmillan College
ISBN:
Category : Computers
Languages : en
Pages : 840
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Book Description
"This book is devoted mainly to applied expert systems. It does cover four additional applied AI Topics: natural language processing, computer vision, speech understanding and intelligent robotics"--Preface
Author: Michael M. Mirabito
Publisher: Taylor & Francis
ISBN: 0240805860
Category : Computers
Languages : en
Pages : 345
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Book Description
A complete explanation of today's communication technologies, and their impact!
Author: Allen Kent
Publisher: CRC Press
ISBN: 9780824722746
Category : Computers
Languages : en
Pages : 448
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Book Description
"This comprehensive reference work provides immediate, fingertip access to state-of-the-art technology in nearly 700 self-contained articles written by over 900 international authorities. Each article in the Encyclopedia features current developments and trends in computers, software, vendors, and applications...extensive bibliographies of leading figures in the field, such as Samuel Alexander, John von Neumann, and Norbert Wiener...and in-depth analysis of future directions."
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
