Sztuczna inteligencja
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
Deep Learning with C++. Design and deploy neural networks using CUDA for high-performance AI in C++
Bill Chen, Vikash Gupta
Deep learning systems often struggle to meet performance demands in real-time and production environments. This book shows you how to build high-performance deep learning systems in C++, enabling efficient and scalable artificial intelligence (AI) in resource-constrained environments where performance matters.You’ll start by setting up a complete C++ deep learning environment and implementing core neural networks from scratch. As you progress, you’ll build advanced architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers, using C++, CUDA, and PyTorch’s C++ API. The book then focuses on model quantization and compression. It will guide you through the model deployment process in production with robust monitoring and explainability. You’ll also explore distributed training and techniques for real-time inference in performance-critical domains.By the end of this book, you’ll be able to design, optimize, and deploy deep learning systems in C++ that are production-ready, scalable, and efficient across multiple industries.*Email sign-up and proof of purchase required
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.
Luis Sanchez, Enrique Fernandez Perdomo, Anil Mahtani
Building and programming a robot can be cumbersome and time-consuming, but not when you have the right collection of tools, libraries, and more importantly expert collaboration. ROS enables collaborative software development and offers an unmatched simulated environment that simplifies the entire robot building process.This book is packed with hands-on examples that will help you program your robot and give you complete solutions using open source ROS libraries and tools. It also shows you how to use virtual machines and Docker containers to simplify the installation of Ubuntu and the ROS framework, so you can start working in an isolated and control environment without changing your regular computer setup.It starts with the installation and basic concepts, then continues with more complex modules available in ROS such as sensors and actuators integration (drivers), navigation and mapping (so you can create an autonomous mobile robot), manipulation, Computer Vision, perception in 3D with PCL, and more. By the end of the book, you’ll be able to leverage all the ROS Kinetic features to build a fully fledged robot for all your needs.
ElevenLabs. Zastosowanie w biznesie i twórczości
Witold Wrotek
To do mnie przemawia Wśród rozmaitych zastosowań sztucznej inteligencji znajdują się możliwości klonowania i generowania głosu. Obecnie na rynku dostępnych jest kilka narzędzi służących do tych celów ― jedno z nich to wciąż zyskująca na popularności platforma ElevenLabs. Stworzył ją zespół pod przewodnictwem dwóch polskich inżynierów: Piotra Dąbkowskiego, który wcześniej pracował dla Google, i Mateusza Staniszewskiego, byłego stratega wdrażania w Palantir Technologies. Inspiracją do powstania ich firmy ElevenLabs była chęć kreowania bardziej realistycznych dubbingów filmowych. Jeśli chcesz się nauczyć klonować głosy w taki sposób, by były nieodróżnialne od prawdziwych — odpowiednio modulowane, o oczekiwanej barwie i intonacji, przekazujące emocje — ta książka jest dla Ciebie. Obsługa narzędzi do generowania głosów AI nie jest trudna, a dzięki temu poradnikowi opanujesz ją błyskawicznie. Z książki dowiesz się między innymi: Dlaczego spośród dostępnych syntezatorów mowy warto wybrać ElevenLabs Jak szybko stworzyć swój pierwszy głosowy komentarz W jaki sposób promptować, by uzyskać naturalnie brzmiące rezultaty Co o klonowaniu i generowaniu głosów mówi prawo Jak przygotowywać nagrania wysokiej jakości ― w tym na potrzeby radia i w różnych językach Jak generować dialogi, podcasty i audiobooki W jaki sposób można zarabiać dzięki syntezie głosu opartej na AI
Suvoraj Biswas
The course begins with an insightful introduction to the burgeoning field of Generative AI, laying down a robust framework for understanding its applications within the AWS ecosystem.The course focuses on meticulously detailing the five pillars of the AWS Well-Architected Framework—Operational Excellence, Security, Compliance, Reliability, and Cost Optimization. Each module is crafted to provide you with a comprehensive understanding of these essential areas, integrating Generative AI technologies. You'll learn how to navigate the complexities of securing AI systems, ensuring they comply with legal and regulatory standards, and designing them for unparalleled reliability. Practical sessions on cost optimization strategies for AI projects will empower you to deliver value without compromising on performance or scalability. Furthermore, the course delves into System Architecture Excellence, emphasizing the importance of robust design principles in creating effective Generative AI solutions.The course wraps up by offering a forward-looking perspective on the Common Architectural Pattern for FM/LLM Integration & Adoption within the AWS framework. You'll gain hands-on experience with AWS solutions specifically tailored for Generative AI applications, including Lambda, API Gateway, and DynamoDB, among others.