Take a look at our Artificial Intelligence books. Shulph carries a great selection of Artificial Intelligence books, and we are always adding more.
Build, train, and deploy intelligent applications using Java libraries Key Features Leverage the power of Java libraries to build smart applications Build and train deep learning models for implementing artificial intelligence Learn various algorithms to automate complex tasks Book Description Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains. What you will learn Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others Build machine learning models using supervised and unsupervised machine learning techniques Implement different deep learning algorithms in Deeplearning4j and build applications based on them Study the basics of heuristic searching and genetic programming Differentiate between syntactic and semantic similarity among texts Perform sentiment analysis for effective decision making with LingPipe Who this book is for Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.
Learn to build intelligent and responsive Non-Player Characters for your games with Unreal Engine Game AI. Key Features Understand the built-in AI systems in Unreal Engine for building intelligent games Leverage the power of Unreal Engine 4 programming to create game AI that focuses on motion, animation, and tactics Learn to profile, visualize, and debug your Game AI for checking logic and optimizing performance Book Description Learning how to apply artificial intelligence ( AI ) is crucial and can take the fun factor to the next level, whether you're developing a traditional, educational, or any other kind of game. If you want to use AI to extend the life of your games and make them challenging and more interesting, this book is for you. The book starts by breaking down AI into simple concepts to get a fundamental understanding of it. Using a variety of examples, you will work through actual implementations designed to highlight key concepts and features related to game AI in UE4. You will learn to work through the built-in AI framework in order to build believable characters for every game genre (including RPG, Strategic, Platform, FPS, Simulation, Arcade, and Educational). You will learn to configure the Navigation, Environmental Querying, and Perception systems for your AI agents and couple these with Behavior Trees, all accompanied with practical examples. You will also explore how the engine handles dynamic crowds. In the concluding chapters, you will learn how to profile, visualize, and debug your AI systems to correct the AI logic and increase performance. By the end of the book, your AI knowledge of the built-in AI system in Unreal will be deep and comprehensive, allowing you to build powerful AI agents within your projects. What you will learn Get an in-depth knowledge about all the AI Systems within Unreal Engine Create complex AIs, understanding the art of designing and developing Behavior Tree Learn how to perform Environmental Queries (EQS) Master the Navigation, Perception, and Crowd Systems Profile and Visualize the AI Systems with powerful debugging tools Extend every AI and Debug system with custom nodes and functions Who this book is for Hands-On Artificial Intelligence with Unreal Engine is for you if you are a game developer with a bit experience in Unreal Engine, and now want to understand and implement believable game AI within Unreal Engine. The book will be both in Blueprint and C++, allowing people from every background to enjoy the book. Whether you're looking to build your first game or expand your knowledge to the edge as a Game AI Programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation, including how to extend some of these systems.
Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book Description Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is for If you're a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key Features Learn advanced concepts in Azure ML and the Cortana Intelligence Suite architecture Explore ML Server using SQL Server and HDInsight capabilities Implement various tools in Azure to build and deploy machine learning models Book Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft's Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you'll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learn Discover the benefits of leveraging the cloud for ML and AI Use Cognitive Services APIs to build intelligent bots Build a model using canned algorithms from Microsoft and deploy it as a web service Deploy virtual machines in AI development scenarios Apply R, Python, SQL Server, and Spark in Azure Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow Implement model retraining in IoT, Streaming, and Blockchain solutions Explore best practices for integrating ML and AI functions with ADLA and logic apps Who this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You'll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
Guide to Marketing Automation and Accelerated ROI on Advertising Key Features - Demonstrates how a DSP works, its bidding strategies, impression tracking, and configurations. - Exemplifies how AI/ML simplifies bidding strategies. - Illustrates how SSP, exchange, ad-server, and header-bidding (client and server-side) work in detail. Description This book provides you with an in-depth understanding of programmatic advertising. This knowledge can be applied to the checklist for procuring the appropriate stack, optimizing existing platforms, and/or building the system from the ground up. With comprehensive treatment of programmatic issues, this book establishes a solid foundation with ID systems, data management systems, and data thinking, among other topics. It explores the different data sources, attributes, and the real-time bidding protocol in detail (RTB steam). It makes its way even further into the larger systems of DSP and SSP. This book will help assist you in all aspects of running an ad-tech system. By the end of this book, you will gain a vast amount of knowledge about programmatic systems. You will become an independent expert that will help you to evaluate the advertising techniques for your own business. What you will learn - Learn about the ID mechanics of cookies and GAID/IDFA. - Gain an intuitive and in-depth understanding of the data's role in AI/ML. - Learn about various data-centric strategies around buy and sell of media. - Learn about DSP, bidder, bidding strategies, RTB, paid impression, and various syncs. - Learn about SSP, Exchange, Ad-Server, header bidding systems, and AI-led floor price optimization. Who this book is for The book is essential for the architects, senior developers, and ad-tech operations to learn about programmatic in-housing from a design, process, strategic thinking, and operational standpoint. It also attracts business professionals who want to learn the tricks of the trade for increasing revenues and learn the art of asking the right questions. Table of Contents 1. ID Management, Cookies, and Sync Mechanics 2. Data and AI Strategies 3. DMP and CDP 4. Exchanges, Ad-Servers, and Header Bidding 5. Bidders and Meta DSPs 6. Data Privacy by Design 7. Buy and Sell Strategies
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent's productivity. As you advance, you'll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you'll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learn Understand how deep learning can be integrated into an RL agent Explore basic to advanced algorithms commonly used in game development Build agents that can learn and solve problems in all types of environments Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem Develop game AI agents by understanding the mechanism behind complex AI Integrate all the concepts learned into new projects or gaming agents Who this book is for If you're a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
Information technology (IT) use has generally been regarded as a positive phenomenon which always generates desirable outcomes. Recent years, however, have witnessed increasing negative consequences resulted from IT use. Emerging issues include individual users have experienced technostress from personal social media usage as well as IT use in the workplace; and organisations have experienced a loss in productivity and assets due to employees’ non-compliance with information security policies.Themes explored throughout the book include: AI and its Implications for Organisations Augmented Reality Cognitive Absorption of Technology Information Technology in Organisations and Societies: Multidisciplinary Perspectives from AI to Technostress represents a collective effort that not only consolidates studies on key issues and phenomena concerning the positive and negative aspects of IT use but also prescribes future research avenues in related research domains. It is particularly relevant to academics and researchers working on IT use research and can be used as a handy reference guide by those working in the field.
Information technology (IT) use has generally been regarded as a positive phenomenon which always generates desirable outcomes. Recent years, however, have witnessed increasing negative consequences resulted from IT use. Emerging issues include individual users have experienced technostress from personal social media usage as well as IT use in the workplace; and organisations have experienced a loss in productivity and assets due to employees’ non-compliance with information security policies.Themes explored throughout the book include: AI and its Implications for Organisations Augmented Reality Social Media Stress Cognitive Absorption of Technology Information Technology in Organisations and Societies: Multidisciplinary Perspectives from AI to Technostress represents a collective effort that not only consolidates studies on key issues and phenomena concerning the positive and negative aspects of IT use but also prescribes future research avenues in related research domains. It is particularly relevant to academics and researchers working on IT use research and can be used as a handy reference guide by those working in the field.
A guide to understand the potential of Intelligent Automation across businesses and enterprises. Key Features - A comprehensive discussion of key concepts, techniques, and key elements of intelligent automation. - Expert coverage on combining various technologies, including RPA, AI, Blockchain, and IoT. - Includes case studies and use cases for successful automation applications. - Precise guidance on how to scale automation in enterprises. Description 'Intelligent Automation Simplified' guides tech professionals to take a much more simplified and sophisticated step towards developing intelligent automation. This book will explain the basic concepts of smart automation and how to put it into practice for a company. This book explores each stage of automation design and explains how these automation fragments can be brought together in the end-to-end automation of workflow. This book discusses numerous examples and scenarios that will help relate and understand how technology can be used in real life to solve business problems. This book provides a lot of information and insights and helps readers grasp the methodology used to develop an automation solution correctly. With detailed illustrations and real use-cases, you will be able to easily create smart automation solutions and practice how to modify them. Towards the end, the book describes how smart automation expands in a company and discusses the various strategies for large-scale use. The book also highlights the latest trends in intelligent automation and its progress into the future of work. What you will learn - Learn about the essential and primary components of intelligent automation. - Investigate the capabilities of RPA and AI in the development of Intelligent Automation solutions. - Recognize the factors that will help you choose the best processes for automation. - Learn how to use the framework to create an Intelligent Automation solution. - Create a blueprint to scale automation in the enterprise. - Discover the most recent Intelligent Automation trends from industry experts. Who this book is for This book is intended for current and future technical professionals who want to learn about Intelligent Automation, plan, and implement it in an enterprise or consult with clients. Readers should be familiar with the software development workflow and have a basic understanding of advanced technologies such as AI and RPA. Table of Contents 1. Introduction to Intelligent Automation 2. Robotic Process Automation 3. Artificial Intelligence in Automation 4. Other technologies in Automation 5. Intelligent Automation Use cases 6. Enterprise Automation Journey 7. Intelligent Automation – Trends and the future
How to minimize the global problem of e-waste Key Features - Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB. - Cutting edge coverage on building intelligent systems for reliability analysis. - Includes numerous techniques and methods to identify failure and reliability parameters. Description Intelligent Reliability Analysis using MATLAB and AI explains a roadmap to analyze and predict various electronic components’ future life and performance reliability. Deeply narrated and authored by reliability experts, this book empowers the reader to deepen their understanding of reliability identification, its significance, preventive measures, and various techniques. The book teaches how to predict the residual lifetime of active and passive components using an interesting use case on electronic waste. The book will demonstrate how the capacity of re-usability of electronic components can benefit the consumer to reuse the same component, with the confidence of successful operations. It lists key attributes and ways to design experiments using Taguchi’s approach, based on various acceleration factors. This book makes it easier for readers to understand reliability modeling of active and passive components using the Artificial Neural Network, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS). The book keeps you engaged with a systematic and detailed explanation of step-wise MATLAB-based implementation of electronic components. What you will learn - Optimize various acceleration factors for exploring the residual life of components experimentally. - Design an intelligent model to predict the upcoming faults and failures of electronic components and make provision for timely replacement of the fault components. - Design experiments using Taguchi’s approach. - Understand reliability modeling of active and passive components using the Artificial Neural Network and Fuzzy Logic. Who this book is for This book is for current and aspiring emerging tech professionals, researchers, students, and anyone who wishes to understand and diagnose the product life of electronic components using the power of artificial intelligence and various experimental techniques. Table of Contents 1. RELIABILITY FUNDAMENTALS 2. RELIABILITY MEASURES 3. REMAINING USEFUL LIFETIME ESTIMATION TECHNIQUES 4. INTELLIGENT MODELS FOR RELIABILITY PREDICTION 5. ACCELERATED LIFE TESTING 6. EXPERIMENTAL TESTING OF ACTIVE AND PASSIVE COMPONENTS 7. INTELLIGENT MODELING FOR RELIABILITY ASSESSMENT USING MATLAB About the Authors Dr Cherry Bhargava is working as an Associate Professor at the Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India. She holds a Ph.D. (ECE) specialization in Artificial Intelligence, M. Tech (VLSI Design & CAD), and B. Tech (EIE) degrees. She is GATE qualified with All India Rank 428. She has authored about 50 technical research papers in SCI, Scopus indexed quality journals, and national/international conferences. She has 18 books to her credit. She has registered six copyrights and filed twenty-one patents. Her four Australian innovation patents are granted. LinkedIn Profile: https://www.linkedin.com/in/dr-cherry-bhargava-7315619/ Dr. Pardeep Kumar Sharma is working as an associate professor at Lovely Professional University, Punjab, India. He has more than 14 years of teaching experience in the field of applied chemistry, artificial intelligence, DOE, and nanotechnology. He has completed his Ph.D. from Lovely Professional University and his post-graduation (Applied Chemistry) from Guru Nanak Dev University, Amritsar. LinkedIn Profile: https://www.linkedin.com/in/dr-pardeep-kumar-sharma-28581818/