For those intrigued by the revolutionary potential of artificial intelligence in the realm of communication, "Fundamentals of ChatGPT for Beginners Using AI" serves as an indispensable manual. This book provides an extensive examination of ChatGPT, a cutting-edge technology that was created by OpenAI. With an initial focus on introductory concepts, this publication gradually advances to more complex subjects, guaranteeing inclusivity for readers of varying proficiency levels. By means of lucid elucidations, pragmatic illustrations, and interactive exercises, readers acquire knowledge regarding the manner in which ChatGPT empowers machines to partake in organic, humanoid dialogues, thereby finding utility across diverse sectors. There are a wide range of applications for ChatGPT, ranging from chatbots for customer support to assistance for creative writing. The book provides users with the knowledge and abilities necessary to train, fine-tune, and deploy ChatGPT models in real-world settings. The book places a strong emphasis on practicality. "Fundamentals of ChatGPT for Beginners Using AI" delivers the information and tools necessary to explore the wonderful world of conversational artificial intelligence, regardless of whether you are a student, a developer, an entrepreneur, or just inquisitive about artificial intelligence.

Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, the book begins with its history, the Turing test, and early applications. Later chapters cover the basics of searching, game playing, and knowledge representation. Expert systems and machine learning are covered in detail, followed by separate programming chapters on Prolog and Python. The concluding chapter on artificial intelligence machines and robotics is comprehensive with numerous modern applications. Features: Covers an introduction to concepts related to AI, including searching processes, knowledge representation, machine learning, expert systems, programming, and robotics Includes separate chapters on Prolog and Python to introduce basic programming techniques in AI

The brain has an inherent advantage over traditional computers in that it possesses the ability to acquire knowledge and skills via the process of learning. Nevertheless, the edge is being swiftly eradicated by a new cohort of artificial intelligence programs known as deep neural networks. The primary objective of this book is to assist readers in comprehending fundamental principles before progressing towards refining their programming abilities, ultimately enabling them to become proficient practitioners in the field of deep learning. The book covers fundamental principles in deep learning, exploring various deep learning designs such as recurrent neural networks. Additionally, it delves into contemporary advancements such as generative adversarial networks. The book serves as a comprehensive manual for the implementation of deep neural networks, including several architectures such as Multilayer Perceptron’s (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and others, inside the frameworks of Kera’s and TensorFlow. This book aims to serve as a concise resource for training and optimising deep neural networks. The book encompasses the foundational principles of neural networks, as well as the training methods used in deep neural networks. The book is highly recommended for students seeking a comprehensive reference manual on deep learning, as well as industry practitioners from many disciplines who want to embark on their data science endeavours.

Machine learning and Artificial Intelligence are pillars on which you can build intelligent applications. This field is essential in the modern world since robots may now display complex cognitive abilities including as decision-making, learning and seeing the environment, behaviour prediction, and language processing. The terms "artificial intelligence" & "machine learning" are often used interchangeably, although they refer to two distinct processes. Machine learning is a branch of artificial intelligence that allows intelligent systems to autonomously learn new things from data, while artificial intelligence as a whole refers to robots that can make choices, acquire new skills, and solve problems. The engineering profession makes extensive use of AI methods to address a broad variety of previously intractable issues. The purpose of this book is to bring together developed form scientists, researchers, and academics to discuss all aspects of artificial intelligence and share their findings with one another and the wider scientific community. The book serves as a leading multidisciplinary forum for discussing real-world problems and the solutions that have been implemented to address them.

We have been curious in teaching computers to learn ever since they were first developed. The implications would be enormous if we knew how to teach them, via programming, to learn and improve automatically with use. Think of personal software assistants that learn their users’ changing interests and then highlight the stories from the online morning newspaper that are most relevant to them based on that information; computers that learn from medical records which treatments are best for new diseases; homes that learn to optimise energy costs based on the unique usage patterns of their occupants. If we could figure out how to teach machines, it would pave the way for all sorts of advanced computing applications and individualised experiences. Human learning skills (and shortcomings) may be better understood with a deeper knowledge of information processing methods for machine learning. Within the recent decade, “machine learning” and “artificial intelligence” have been widely used in a variety of settings. Both phrases are widely used in the scientific and media communities, often with overlapping but not always synonymous meanings. The authors of this book set out to define the terminologies at play here and, more specifically, to outline the role that machine learning plays in AI. The authors provide a literature analysis and a conceptual framework that explain how machine learning contributes to the development of (artificial) intelligent agents.

Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources. What you will learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

AI-900: Microsoft Azure AI Fundamentals Exam Preparation Book New Edition 2024: Achieve success in your Microsoft AI-900 Azure AI Fundamentals Exam on the first try with our new and exclusive preparation book. This Exclusive Book is a preparation for students who want to Successfully pass the AI-900: Microsoft Azure AI Fundamentals exam on the first Try! Here we’ve brought Top new and recurrent Exam Practice Questions for Microsoft AI-900 Azure AI Fundamentals exam so that you can prepare well for this exam. This Exclusive book is aligned with the Microsoft AI-900 Exam Review Manual 2024 edition and covers all the exam’s topics that a Microsoft AI-900 candidate needs to understand in order to pass the exam successfully. The book practice tests contain exclusive, up-to-date content that is designed to match the real exam. The Practice tests will help you gaining more knowledge and more confidence on exam preparation. You will be able to self-evaluation against the real exam content. This book of exclusive practice tests will test you on questions asked in the actual Exam. This exam is intended for candidates no matter their prior knowledge or experience. Skills measured: Describe AI workloads and considerations (15-20%) Describe the fundamental principles of machine learning (ML) on Azure (30-35%) Describe the features of the computer vision (CV) workloads on Azure (15-20%) Describe the features of the Natural Language Processing (NLP) workloads on Microsoft Azure (15-20%) Describe features of generative AI workloads on Azure (15-20%) Who this book is for: Microsoft Azure AI Fundamentals (AI-900) candidates. Beginners in Machine Learning (ML) Welcome!