Sztuczna inteligencja
Denis Rothman
Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios.Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence.By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence.*Email sign-up and proof of purchase required
Dr. Alex Antic, John K. Thompson
A Gartner prediction in 2018 led to numerous articles stating that 85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic?The demand for data scientists has only grown since 2015, when they were dubbed the new “rock stars” of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer?Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs.Interviews with: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veličković, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, and Stephane Doyen.
CSS3 and SVG with Meta AI. AI-Driven CSS3 and SVG Design Techniques for Modern Web Solutions
Mercury Learning and Information, Oswald Campesato
This book introduces the innovative integration of CSS3 and SVG with generative AI tools, providing a foundation for modern web design. Readers begin by exploring the basics of generative AI and prompt engineering, gaining insights into how these technologies influence visual storytelling and creativity in web design.Progressing through the chapters, the book covers essential CSS3 concepts, including 3D animations and dynamic styling, before delving into advanced SVG techniques for creating scalable and responsive vector graphics. With practical examples, readers learn to merge CSS3 and SVG, enabling the development of seamless AI-enhanced animations and graphics tailored to modern design needs. The integration of Meta AI is highlighted, showcasing its role in enhancing workflows and achieving innovative solutions.By the end, readers will have gained the skills to create cutting-edge, scalable, and visually engaging web designs. The book equips developers with the knowledge and tools to incorporate AI-driven enhancements into their projects, ensuring designs remain both innovative and practical for real-world applications.
Greg Deckler
DAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs.You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient.By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics.
Cher Simon
Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that’ll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you’ll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you’ll get equipped with XAI and anomaly detection knowledge that’ll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you’ll learn how to quantify and assess their explainability.By the end of this deep learning book, you’ll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.
Upendra Kumar Devisetty
Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets.By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.
Vitor Cerqueira, Luís Roque
Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.
Deep Learning from the Basics. Python and Deep Learning: Theory and Implementation
Koki Saitoh, Shigeo Yushita
Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us.Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You’ll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you’ll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays.By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning.
Alan M. F. Souza, Fabio M. Soares,...
Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:1. Java Deep Learning Essentials 2. Machine Learning in Java3. Neural Network Programming with Java, Second Edition
Andy Peng, Alex Strick van Linschoten, Duarte...
Learn how to build, fine-tune, and deploy AI systems using DeepSeek, one of the most influential open-source large language models available today. This book guides you through real-world DeepSeek applications—from understanding its core architecture and training foundations to developing reasoning agents and deploying production-ready systems.Starting with a concise synthesis of DeepSeek's research, breakthroughs, and open-source philosophy, you’ll progress to hands-on projects including prompt engineering, workflow design, and rationale distillation. Through detailed case studies—ranging from document understanding to legal clause analysis—you’ll see how to use DeepSeek in high-value GenAI scenarios.You’ll also learn to build sophisticated agent workflows and prepare data for fine-tuning. By the end of the book, you’ll have the skills to integrate DeepSeek into local deployments, cloud CI/CD pipelines, and custom LLMOps environments.Written by experts with deep knowledge of open-source LLMs and deployment ecosystems, this book is your comprehensive guide to DeepSeek’s capabilities and implementation.
Developing AI Applications. An Introduction
Rheinwerk Publishing, Inc, Metin Karatas
This book opens with a clear introduction to AI fundamentals, covering its history and key concepts while guiding readers through installing essential tools like KNIME and AutoKeras. It begins by building a strong foundation in artificial neural networks and decision trees, enabling readers to grasp core AI methods. The journey then advances to convolutional layers for image classification, transfer learning, and anomaly detection, offering practical, beginner-friendly examples.As the reader progresses, the book explores text classification, cluster analysis, and automated AI model creation with AutoKeras. Visual programming with KNIME is introduced to simplify complex AI workflows. Further chapters cover reinforcement learning and genetic algorithms, expanding the reader’s skill set and preparing them for more advanced challenges. Hands-on exercises throughout reinforce concepts and practical application.In its final chapters, the guide dives into cutting-edge AI tools by demonstrating how to leverage ChatGPT and DALL-E APIs, including prompt engineering and API programming. It concludes with an outlook on the future of AI, equipping readers with the knowledge and confidence to build and deploy their own AI-powered applications from start to finish.
Tim Beattie, Mike Hepburn, Noel O'Connor, Donal...
DevOps Culture and Practice with OpenShift features many different real-world practices - some people-related, some process-related, some technology-related - to facilitate successful DevOps, and in turn OpenShift, adoption within your organization. It introduces many DevOps concepts and tools to connect culture and practice through a continuous loop of discovery, pivots, and delivery underpinned by a foundation of collaboration and software engineering.Containers and container-centric application lifecycle management are now an industry standard, and OpenShift has a leading position in a flourishing market of enterprise Kubernetes-based product offerings. DevOps Culture and Practice with OpenShift provides a roadmap for building empowered product teams within your organization.This guide brings together lean, agile, design thinking, DevOps, culture, facilitation, and hands-on technical enablement all in one book. Through a combination of real-world stories, a practical case study, facilitation guides, and technical implementation details, DevOps Culture and Practice with OpenShift provides tools and techniques to build a DevOps culture within your organization on Red Hat's OpenShift Container Platform.
Digital Ethics in the Age of AI. Navigating the ethical frontier today and beyond
IT Governance Publishing, Dr. Julie E. Mehan
Digital Ethics in the Age of AI explores the profound ethical challenges posed by the rise of artificial intelligence and its integration into our daily lives. The book covers AI’s disruptive effects across various sectors, including misinformation, privacy, and job displacement, offering clear explanations and real-world examples. The author delves into the role of AI in spreading misinformation and disinformation, including the creation of deepfakes, and highlights the increasing risk of online disinhibition driven by AI-powered interactions. The book also addresses the cognitive biases embedded within AI systems and the growing concerns over privacy, data security, and surveillance in an age of ubiquitous AI technologies. Finally, the book explores the potential for AI-driven job displacement, particularly in the cognitive class, and the societal implications of such disruptions. It also covers intellectual property challenges in the age of AI and the complexities surrounding generative AI’s impact on privacy and digital ownership. Offering solutions for mitigating these risks, Digital Ethics in the Age of AI provides a roadmap for navigating the ethical and regulatory landscape of AI today and in the future.
Gerard Johansen
Digital Forensics and Incident Response will guide you through the entire spectrum of tasks associated with incident response, starting with preparatory activities associated with creating an incident response plan and creating a digital forensics capability within your own organization. You will then begin a detailed examination of digital forensic techniques including acquiring evidence, examining volatile memory, hard drive assessment, and network-based evidence. You will also explore the role that threat intelligence plays in the incident response process. Finally, a detailed section on preparing reports will help you prepare a written report for use either internally or in a courtroom.By the end of the book, you will have mastered forensic techniques and incident response and you will have a solid foundation on which to increase your ability to investigate such incidents in your organization.
Nelson Enriquez, Samundar Singh Rathore
Business Intelligence is a type of technology that has been proven to support business decisions in an organization. MicroStrategy 9 is a fully-integrated BI platform that makes Business Intelligence faster, easier, and more user-friendly. It enables businesses to generate their own reports and dashboards without the need for technical knowledge.This practical, hands-on guide will provide Business Intelligence for executives, as well as enable BI reports and dashboards without the dependency of IT savvy personnel. It will allow you to design, build, and share business relevant data in hours, in a secure way, including mobile devices and show you how to leverage your transactional information.This example-oriented book looks at the value proposition of cloud computing and the MicroStrategy platform, and features practical exercises for BI reports and dashboard enablement, including the design phase and best practices for when we design a BI report.The book begins with an exploration of MicroStrategy along with typical business needs. Our focus then shifts to best practices for BI reports and dashboard definitions from the functional stand point, with easy-to-do exercises that will allow you to enable the reports in the platform. You will learn about scorecards and dashboards, along with sharing the reports. Next, you will get acquainted with cloud-based services provided by the MicroStrategy platform. By the end of this book, you will able to design, enable, and share BI reports and dashboards without the need for comprehensive technical knowledge, and leverage the latest technology on the market.
DZIENNIK(AI)RSTWO. Jak sztuczna inteligencja zmienia najciekawszą profesję na świecie
Jan Kreft
Za banałem rewolucji sztucznej inteligencji (AI) kryją się obietnice, lęki i marzenia o nirwanie końca ułomności dziennikarstwa i bogactwie możliwości. Dziennikarstwo algorytmiczne, dziennikarstwo danych, burzy fundamenty modeli biznesowych i zarządzania mediami, tożsamości i ról zawodowych oraz dziennikarskiej ideologii. Obiecuje wiele, destabilizuje wszystko. Zapowiada egalitaryzm, oferuje niespotykaną historycznie dominację. To opowieść o tym, co nauka ma do powiedzenia o maszynach "płynnych bzdur" i chatbotach nie tylko w roli "służalczych kretynów". O schyłku hegemonii dziennikarstwa, o kreatywności i wiarygodności sztucznej inteligencji w dziennikarstwie, stereotypach na temat dezinformacji i powszechnym zastępowaniu i uzupełnianiu dziennikarza w jego zadaniach. O autorytecie algorytmicznym oraz szkoleniu sztucznej inteligencji i treściach nie do odróżnienia od dzieł dziennikarzy. O inwazji "obcych" na jedną z najbardziej pasjonujących profesji, pożytkach teorii ludowych i teorii spiskowych. Także o tym, czym jest prawda sztucznej inteligencji. Wreszcie o przyszłości, oporze algorytmicznym i sensie wykonywania zawodu dziennikarza na świecie i w Polsce. Książka prof. Jana Krefta DZIENNIK(AI)RSTWO. Jak sztuczna inteligencja zmienia najciekawszą profesję na świecie jest niewątpliwie skazana na sukces, podejmując problematykę tzw. sztucznej inteligencji, która stała się obecnie tematem o pierwszoplanowym charakterze i jest rozpatrywana w różnych kontekstach: prawnym, ekonomicznym, społecznym. Entuzjazm i jednocześnie niepokój, jakie wywołuje, można porównać do podobnej gorączki, która towarzyszyła rozwojowi Internetu pod koniec lat 90. Książka, która podejmuje ten temat, wpisuje się natychmiast w gorącą atmosferę oczekiwań i lęków, co z pewnością przełoży się na jej popularność, a także [...] będzie wyznaczać przyszłe tory refleksji, definiować podstawowe pole analizy czy podsuwać główne pojęcia z tą refleksją związane. Z recenzji prof. Rafała Maciąga, Uniwersytet Jagielloński Prof. dr hab. Jan Kreft - wieloletni dziennikarz i korespondent zagraniczny, menedżer zarządzający firmami mediów i nowych technologii. Szef Zakładu Zarządzania Algorytmicznego (Politechnika Gdańska). Autor kilkunastu książek, między innymi: Władza algorytmów, Władza platform, Za fasadą społeczności, Koniec dziennikarstwa, jakie znamy oraz Władza misjonarzy. Zmierzch i świt świeckiej religii w Dolinie Krzemowej.