Books: 31

Artificial Intelligence

CoverTitleYear
This edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize more » the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years. « less
2017
A Practitioner's Approach
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 more » 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 « less
2017
A Machine Intelligence Approach
This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given more » time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers’ ability and understanding of the topics covered. « less
2017
Create Facebook and Skype Chatbots using Microsoft Visual Studio and C#
The Microsoft Bot Framework allows you to easily create bots. This book covers using Visual Studio 2015 to create Chatbots using the Microsoft Bot Framework. The purpose of this book is to demonstrate, provide examples of, and to explain important concepts of the technology. You can create bots that more » interact with your users naturally wherever they are, including Facebook, text, Skype, Office 365 email, and other popular services. Chapter 1: Understanding the Microsoft Bot Framework Chapter 2: Create a Hello World! Bot Chapter 3: Using FormFlow Chapter 4: Using Dialogs Chapter 5: Using Images, Cards, Carousels, and Buttons Chapter 6: Implementing A SQL Server Database With Your Bot Chapter 7: Implementing Language Understanding Intelligent Service (LUIS) Chapter 8: Calling The Microsoft Bot Framework Using The Direct Line API Chapter 9: Using Application Insights To Monitor Your Bot Chapter 10: Creating a Skype Bot Chapter 11: Creating A Facebook Messenger Bot « less
2016
This book attempts to connect artificial intelligence to primitive intelligence. It explores the idea that a genuinely intelligent computer will be able to interact naturally with humans. To form this bridge, computers need the ability to recognize, understand and even have instincts similar to humans. more » The author organizes the book into three parts. He starts by describing primitive problem-solving, discussing topics like default mode, learning, tool-making, pheromones and foraging. Part two then explores behavioral models of instinctive cognition by looking at the perception of motion and event patterns, appearance and gesture, behavioral dynamics, figurative thinking, and creativity. The book concludes by exploring instinctive computing in modern cybernetics, including models of self-awareness, stealth, visual privacy, navigation, autonomy, and survivability. Instinctive Computing reflects upon systematic thinking for designing cyber-physical systems and it would be a stimulating reading for those who are interested in artificial intelligence, cybernetics, ethology, human-computer interaction, data science, computer science, security and privacy, social media, or autonomous robots. « less
2016
This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters more » 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube. « less
2016
A hands-on introduction to learning algorithms
This book is a hands-on introduction to learning algorithms. It is for people who may know a little machine learning (or not) and who may have heard about TensorFlow, but found the documentation too daunting to approach. The learning curve is gentle and you always have some code to illustrate the math more » step-by-step. TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book starts with the absolute basics of TensorFlow. We found that most tutorials on TensorFlow start by attempting to teach both machine learning concepts and TensorFlow terminology at the same time. Here we first make sure you've had the opportunity to become comfortable with TensorFlow's mechanics and core API before covering machine learning concepts. « less
2016
This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented more » by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book’s website. « less
2015
Deep Learning and Neural Networks
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such more » as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization. « less
2015
Enabling Methodologies for Proactive and Self-Organizing Power Systems
This book considers the emerging technologies and methodologies of the application of computational intelligence to smart grids. From a conceptual point of view, the smart grid is the convergence of information and operational technologies applied to the electric grid, allowing sustainable options to more » customers and improved levels of security. Smart grid technologies include advanced sensing systems, two-way high-speed communications, monitoring and enterprise analysis software, and related services used to obtain location-specific and real-time actionable data for the provision of enhanced services for both system operators (i.e. distribution automation, asset management, advanced metering infrastructure) and end-users (i.e. demand side management, demand response). In this context, a crucial issue is how to support the evolution of existing electrical grids from static hierarchal systems to self-organizing, highly scalable and pervasive networks. Modern trends are oriented toward the employment of computational intelligence techniques for deploying advanced control, protection and monitoring architectures that move away from the older centralized paradigm to systems distributed across the field with an increasing pervasion of intelligence devices. The large-scale deployment of computational intelligence technologies in smart grids could lead to a more efficient tasks distribution amongst energy resources and, consequently, to a sensible improvement of the electrical grid flexibility. Readership: Graduate students and researchers interested in smart grids and advanced power networks. « less
2015