Hands-On Simulation Modeling with Python. Develop simulation models for improved efficiency and precision in the decision-making process - Second Edition

Giuseppe Ciaburro

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

Hyperledger Cookbook. Over 40 recipes implementing the latest Hyperledger blockchain frameworks and tools

Xun (Brian) Wu, Chuanfeng Zhang, Zhibin (Andrew) Zhang

Hyperledger is an open-source project and creates private blockchain applications for a range of domains. This book will be your desk reference as you explore common and not-so-common challenges faced while building blockchain networks using Hyperledger services.We'll work through all Hyperledger platform modules to understand their services and features and build end-to-end blockchain applications using various frameworks and tools supported by Hyperledger. This book's independent, recipe-based approach (packed with real-world examples) will familiarize you with the blockchain development cycle. From modeling a business network to integrating with various tools, you will cover it all. We'll cover common and not-so-common challenges faced in the blockchain life cycle. Later, we'll delve into how we can interact with the Hyperledger Fabric blockchain, covering all the principles you need to master, such as chaincode, smart contracts, and much more. We'll also address the scalability and security issues currently faced in blockchain development.By the end of this book, you will be able to implement each recipe to plan, design, and create a full-fledged, private, decentralized application to meet organizational needs.

Hyperparameter Tuning with Python. Boost your machine learning model’s performance via hyperparameter tuning

Louis Owen

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements.You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter.By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.

Implementing Microsoft Dynamics 365 Customer Engagement. Configure, customize, and extend Dynamics 365 CE in order to create effective CRM solutions

Mahender Pal

Microsoft Dynamics 365 for Customer Engagement (CE) is one of the leading customer relationship management (CRM) solutions that help companies to effectively communicate with their customers and allows them to transform their marketing strategies. Complete with detailed explanations of the essential concepts and practical examples, this book will guide you through the entire life cycle of implementing Dynamics 365 CE for your organization or clients, and will help you avoid common pitfalls while increasing efficiency at every stage of the project. Starting with the foundational concepts, the book will gradually introduce you to Microsoft Dynamics 365 features, plans, and products. You’ll learn various implementation strategies and requirement gathering techniques, and then design the application architecture by converting your requirements into technical and functional designs. As you advance, you’ll learn how to configure your CRM system to meet your organizational needs, customize Dynamics 365 CE, and extend its capabilities by writing client-side and server-side code. Finally, you’ll integrate Dynamics 365 CE with other applications and explore its business intelligence capabilities. By the end of this Microsoft Dynamics 365 book, you’ll have gained an in-depth understanding of all the key components necessary for successful Dynamics 365 CE implementation.

Implementing Splunk. A comprehensive guide to help you transform Big Data into valuable business insights with Splunk 6.2

James D. Miller, VINCENT BUMGARNER

Industrial Digital Transformation. Accelerate digital transformation with business optimization, AI, and Industry 4.0

Shyam Varan Nath, Ann Dunkin, Mahesh Chowdhary, Nital Patel

Digital transformation requires the ability to identify opportunities across industries and apply the right technologies and tools to achieve results. This book is divided into two parts with the first covering what digital transformation is and why it is important. The second part focuses on how digital transformation works.After an introduction to digital transformation, you will explore the transformation journey in logical steps and understand how to build business cases and create productivity benefit statements. Next, you’ll delve into advanced topics relating to overcoming various challenges. Later, the book will take you through case studies in both private and public sector organizations. You’ll explore private sector organizations such as industrial and hi-tech manufacturing in detail and get to grips with public sector organizations by learning how transformation can be achieved on a global scale and how the resident experience can be improved. In addition to this, you will understand the role of artificial intelligence, machine learning and deep learning in digital transformation. Finally, you’ll discover how to create a playbook that can ensure success in digital transformation.By the end of this book, you’ll be well-versed with industrial digital transformation and be able to apply your skills in the real world.

Integrate Lua with C++. Seamlessly integrate Lua scripting to enhance application flexibility

Wenhuan Li

C++ is a popular choice in the developer community for building complex and large-scale performant applications and systems. Often a need arises to extend the system at runtime, without recompiling the whole C++ program. Using a scripting language like Lua can help achieve this goal efficiently.Integrate Lua to C++ is a comprehensive guide to integrating Lua to C++ and will enable you to achieve the goal of extending C++ programs at runtime. You’ll learn, in sequence, how to get and compile the Lua library, the Lua programming language, calling Lua code from C++, and calling C++ code from Lua. In each topic, you’ll practice with code examples, and learn the in-depth mechanisms for smooth working. Throughout the book, the latter examples build on the earlier ones while also acting as a standalone. You’ll learn to implement Lua executor and Lua binding generator, which you can use in your projects directly with further customizations.By the end of this book, you’ll have mastered integrating Lua into C++ and using Lua in your C++ project efficiently, gained the skills to extend your applications at runtime, and achieved dynamic and adaptable C++ development.

Intelligent Document Processing with AWS AI/ML. A comprehensive guide to building IDP pipelines with applications across industries

Sonali Sahu

With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs.This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems.By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation.

IoT and Edge Computing for Architects. Implementing edge and IoT systems from sensors to clouds with communication systems, analytics, and security - Second Edition

Perry Lea

Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being. An architectural guide is needed if you want to traverse the spectrum of technologies needed to build a successful IoT system, whether that's a single device or millions of IoT devices. IoT and Edge Computing for Architects, Second Edition encompasses the entire spectrum of IoT solutions, from IoT sensors to the cloud. It examines modern sensor systems, focusing on their power and functionality. It also looks at communication theory, paying close attention to near-range PAN, including the new Bluetooth® 5.0 specification and mesh networks. Then, the book explores IP-based communication in LAN and WAN, including 802.11ah, 5G LTE cellular, Sigfox, and LoRaWAN. It also explains edge computing, routing and gateways, and their role in fog computing, as well as the messaging protocols of MQTT 5.0 and CoAP.With the data now in internet form, you'll get an understanding of cloud and fog architectures, including the OpenFog standards. The book wraps up the analytics portion with the application of statistical analysis, complex event processing, and deep learning models. The book then concludes by providing a holistic view of IoT security, cryptography, and shell security in addition to software-defined perimeters and blockchains.

Java Deep Learning Essentials. Unlocking the next generation of predictive power

Yusuke Sugomori

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It’s something that’s moving beyond the realm of data science – if you’re a Java developer, this book gives you a great opportunity to expand your skillset.Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you’ve got to grips with the fundamental mathematical principles, you’ll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you’ll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today.By the end of the book, you’ll be ready to tackle Deep Learning with Java. Wherever you’ve come from – whether you’re a data scientist or Java developer – you will become a part of the Deep Learning revolution!

Języki i paradygmaty programowania. Teoria i praktyka

Feliks Kurp

Czym w rzeczywistości jest programowanie? I jak zacząć programować? Oprogramowanie jest dziś praktycznie wszędzie, a programiści od dawna należą do najbardziej poszukiwanych specjalistów. Na podstawie napisanego przez nich kodu funkcjonują już nie tylko komputery i smartfony. Oprogramowanie steruje sprzętami domowymi, telewizorem czy lodówką. W ramach tak zwanego internetu rzeczy wiele urządzeń technicznych komunikuje się między sobą bez udziału człowieka. Gwałtownie rozwija się sztuczna inteligencja, wymagająca specjalistycznego oprogramowania. Nie dziwi więc, że jego rozwój ciągle przyspiesza. W obliczu tych faktów odpowiedź na pytanie, jakiego języka programowania warto się nauczyć, jest trudna. Nawet dla osoby, która wie, w jaki sposób zamierza w przyszłości korzystać ze swoich informatycznych umiejętności. Autor książki proponuje nieco inne podejście do nauki programowania. Zachęca do zapoznania się z podstawowymi własnościami i możliwymi zastosowaniami kilku odległych od siebie, ale niezwykle ważnych aktualnie języków programowania, takich jak Python, Java SE, JavaScript i Prolog. W trakcie ich poznawania czytelnicy będą mieli okazję zgłębić filozofię programowania, a równocześnie zdobywać praktyczne umiejętności programistyczne na podstawowym poziomie. Starannie dobrany kod pokazuje możliwe zastosowania wybranych języków programowania. Pomoc w nauce stanowią też zadania do samodzielnego rozwiązania.

Large Language Models An Introduction. Understanding the Fundamentals and Applications of Generative AI

Mercury Learning and Information, Oswald Campesato

This book provides a comprehensive overview of large language models (LLMs) and generative AI, covering foundational principles, advanced concepts, and practical applications. It begins with an introduction to the generative AI landscape, examining key features, popular techniques, and its influence on industries like art, business, and technology. The book also explores prominent models like ChatGPT and GPT-4, detailing their architectures, capabilities, and applications in text generation, code assistance, and conversational AI.Additionally, the book traces the evolution of LLMs, particularly focusing on the BERT family and its wide-ranging applications in natural language processing, translation, and multilingual tasks. Readers will also gain expertise in prompt engineering, learning practical tips and examples to interact more effectively with LLMs.In later chapters, the book tackles advanced topics such as fine-tuning LLMs, working with SVG, and concepts like few-shot learning and reinforcement learning. It concludes with discussions on ethical issues, biases in generative AI, and emerging trends like AI’s integration with IoT, quantum computing, and neuromorphic computing. This guide equips professionals and enthusiasts with a thorough understanding of generative AI's present and future.

Learn OpenAI Whisper. Transform your understanding of GenAI through robust and accurate speech processing solutions

Josué R. Batista, Christopher Papile

As the field of generative AI evolves, so does the demand for intelligent systems that can understand human speech. Navigating the complexities of automatic speech recognition (ASR) technology is a significant challenge for many professionals. This book offers a comprehensive solution that guides you through OpenAI's advanced ASR system.You’ll begin your journey with Whisper's foundational concepts, gradually progressing to its sophisticated functionalities. Next, you’ll explore the transformer model, understand its multilingual capabilities, and grasp training techniques using weak supervision. The book helps you customize Whisper for different contexts and optimize its performance for specific needs. You’ll also focus on the vast potential of Whisper in real-world scenarios, including its transcription services, voice-based search, and the ability to enhance customer engagement. Advanced chapters delve into voice synthesis and diarization while addressing ethical considerations.By the end of this book, you'll have an understanding of ASR technology and have the skills to implement Whisper. Moreover, Python coding examples will equip you to apply ASR technologies in your projects as well as prepare you to tackle challenges and seize opportunities in the rapidly evolving world of voice recognition and processing.

Learning Game AI Programming with Lua. Leverage the power of Lua programming to create game AI that focuses on motion, animation, and tactics

David Young

Learning OpenCV 3 Computer Vision with Python. Unleash the power of computer vision with Python using OpenCV

Gionata Minichino, Joseph Howse, Gil Levi, Joe Minichino

Machine Learning: End-to-End guide for Java developers. Data Analysis, Machine Learning, and Neural Networks simplified

Boštjan Kaluža, Krishna Choppella, Uday Kamath

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:[*]Java for Data Science[*]Machine Learning in Java [*]Mastering Java Machine LearningOn completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.

Machine Learning for Streaming Data with Python. Rapidly build practical online machine learning solutions using River and other top key frameworks

Joos Korstanje

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.

Machine learning i natural language processing w programowaniu. Podręcznik z ćwiczeniami w Pythonie

Piotr Wróblewski

Wejdź na nowy poziom programowania z ML i NLP Sztuczna inteligencja stale się rozwija. Właściwie codziennie słyszymy o jej rosnących możliwościach, nowych osiągnięciach i przyszłości, jaką nam przyniesie. Jednak w tej książce skupiamy się nie na przyszłości, a na teraźniejszości i praktycznym obliczu AI - na usługach, które świadczy już dziś. Większość najciekawszych zastosowań sztucznej inteligencji bazuje na ML (uczenie maszynowe, ang. machine learning), NLP (przetwarzanie języka naturalnego, ang. natural language processing) i architekturze RAG (ang. retrieval augmented generation) zwiększającej możliwości tzw. dużych modeli językowych (LLM, ang. large language model). Stanowią one podwaliny budowy systemów AI, bez których te systemy często wcale nie mogłyby powstać. Do niedawna ML i NLP pozostawały domeną badaczy i specjalistów - znajdowały się poza zasięgiem praktyków programowania. Aktualnie jest inaczej, szybkie komputery, pojemne pamięci RAM i zaawansowane procesory pozwalają stosować te technologie w codziennej pracy programisty. Szczególnie programisty języka Python, do którego są one niemal "naturalnie" przypisane. Mało tego, od kodujących w Pythonie coraz częściej wręcz wymaga się umiejętności znajomości obszaru AI. Tym bardziej warto sięgnąć po ten podręcznik z ćwiczeniami, dzięki któremu między innymi: Dowiesz się, jak używać Pythona do rozwiązywania problemów AI Poznasz tajniki analizy tekstów, analizy sentymentu Zrozumiesz, jak skutecznie używać algorytmów klasyfikacji, regresji i grupowania do rozwiązywania problemów biznesowych Pokonwersujesz z ChatGPT - i to bez wchodzenia na stronę internetową tego serwisu

Machine Learning Security Principles. Keep data, networks, users, and applications safe from prying eyes

John Paul Mueller, Rod Stephens

Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies.This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.

Managing Risks in Digital Transformation. Navigate the modern landscape of digital threats with the help of real-world examples and use cases

Ashish Kumar, Shashank Kumar, Abbas Kudrati, Sarah Armstrong- Smith

With the rapid pace of digital change today, especially since the pandemic sped up digital transformation and technologies, it has become more important than ever to be aware of the unknown risks and the landscape of digital threats. This book highlights various risks and shows how business-as-usual operations carried out by unaware or targeted workers can lead your organization to a regulatory or business risk, which can impact your organization’s reputation and balance sheet.This book is your guide to identifying the topmost risks relevant to your business with a clear roadmap of when to start the risk mitigation process and what your next steps should be. With a focus on the new and emerging risks that remote-working companies are experiencing across diverse industries, you’ll learn how to manage risks by taking advantage of zero trust network architecture and the steps to be taken when smart devices are compromised. Toward the end, you’ll explore various types of AI-powered machines and be ready to make your business future-proof.In a nutshell, this book will direct you on how to identify and mitigate risks that the ever- advancing digital technology has unleashed.

Mastering Apex Programming. A developer’s guide to learning advanced techniques and best practices for building robust Salesforce applications

Paul Battisson, Mike Wheeler

As applications built on the Salesforce platform are now a key part of many organizations, developers are shifting focus to Apex, Salesforce’s proprietary programming language. As a Salesforce developer, it is important to understand the range of tools at your disposal, how and when to use them, and best practices for working with Apex. Mastering Apex Programming will help you explore the advanced features of Apex programming and guide you in delivering robust solutions that scale.This book starts by taking you through common Apex mistakes, debugging, exception handling, and testing. You'll then discover different asynchronous Apex programming options and develop custom Apex REST web services. The book shows you how to define and utilize Batch Apex, Queueable Apex, and Scheduled Apex using common scenarios before teaching you how to define, publish, and consume platform events and RESTful endpoints with Apex. Finally, you'll learn how to profile and improve the performance of your Apex application, including architecture trade-offs.With code examples used to facilitate discussion throughout, by the end of the book, you'll have developed the skills needed to build robust and scalable applications in Apex.

Mastering QuickBooks 2020. The ultimate guide to bookkeeping and QuickBooks Online

Crystalynn Shelton

Intuit QuickBooks is an accounting software package that helps small business owners to manage all their bookkeeping tasks. Its complete range of accounting capabilities, such as tracking income and expenses, managing payroll, simplifying taxes, and accepting online payments, makes QuickBooks software a must-have for business owners and aspiring bookkeepers.This book is a handy guide to using QuickBooks Online to manage accounting tasks and drawing business insights by generating reports easily. Using a fictitious company, the book demonstrates how to create a QuickBooks Online account; customize key settings for a business; manage customers, vendors, and products and services; generate reports; and close the books at the end of the period. QuickBooks records your debits and credits, so you don't need to learn accounting. However, you will find out what happens under the hood and understand how your financial statements are impacted by what you do in QuickBooks. You'll also discover tips, shortcuts, and best practices that will help you save time and become a QuickBooks pro.By the end of this book, you'll have become well-versed with the features of QuickBooks and be able to set up your business and manage all bookkeeping tasks with ease.

Matematyczne modele uczenia maszynowego w językach MATLAB i PYTHON

Stanisław Osowski, Robert Szmurło

Prezentowane opracowanie dotyczy różnych modeli i metod stosowanych w uczeniu maszynowym. W szczególności, w poszczególnych rozdziałach przedstawione są takie zagadnienia, jak: regresja liniowa; klasyfikatory KNN; klasyfikatory Bayesa; modele matematyczne drzew decyzyjnych; sieci neuronowe MLP; sieci RBF; sieci SVM do klasyfikacji i regresji; sieci głębokie (CNN, autoenkoder, LSTM, transformer); zagadnienia zdolności generalizacyjnych modeli, w tym zespoły klasyfikatorów i systemów regresyjnych; transformacje i metody redukcji wymiaru danych wielowymiarowych; metody grupowania danych wielowymiarowych; wybrane metody generacji i selekcji cech diagnostycznych; metody oceny jakości rozwiązań; podstawowe rozwiązania adaptacyjnych systemów rozmytych. W przedstawieniu poszczególnych rozwiązań modelowych zaprezentowano zarówno strukturę pod-stawowych modeli, jak i algorytmy uczące dostosowane do konkretnego modelu. Ponieważ z punktu widzenia aktualnego stanu wiedzy do najważniejszych rozwiązań sztucznej inteligencji należą sztuczne sieci neuronowe. Tym zagadnieniom poświęcono najwięcej uwagi, wprowadzając różne rozwiązania sieciowe, w tym perceptron wielowarstwowy (MLP), sieć o radialnej funkcji bazowej (RBF), maszynę wektorów nośnych (SVM) czy różne rozwiązania głębokich sieci neuronowych wielowarstwowych, takich jak sieć konwolucyjna (CNN), autoenkoder (AE) czy sieć LSTM. Teoretyczne podstawy algorytmów uczących zostały zilustrowane przykładowymi programami implementującymi je przy użyciu oprogramowania Matlab i Python. Prezentowane w podręczniku skrypty z przykładami w Matlabie i Pythonie zostały udostępnione na platformie Github pod adresem: https://github.com/szmurlor/mmum. Podręcznik jest przeznaczony dla słuchaczy wyższych lat studiów, doktorantów i ludzi zainteresowanych metodami uczenia maszynowego, podstawowego narzędzia sztucznej inteligencji. Ze względu na interdyscyplinarny charakter tematyki może być wykorzystany zarówno w informatyce, inżynierii biomedycznej, jak i innych naukach technicznych. Wprowadzenie zarówno podstawowych jak i zaawansowanych pojęć uczenia maszynowego powoduje, że może być użyteczny dla osób początkujących i zaawansowanych w tej tematyce.

Microsoft Azure AI Fundamentals AI-900 Exam Guide. Gain proficiency in Azure AI and machine learning concepts and services to excel in the AI-900 exam

Aaron Guilmette, Steve Miles, Peter De Tender

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.