Informatyka
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LLVM Code Generation. A deep dive into compiler backend development
Quentin Colombet, Kristof Beyls
The LLVM infrastructure is a popular compiler ecosystem widely used in the tech industry and academia. This technology is crucial for both experienced and aspiring compiler developers looking to make an impact in the field. Written by Quentin Colombet, a veteran LLVM contributor and architect of the GlobalISel framework, this book provides a primer on the main aspects of LLVM, with an emphasis on its backend infrastructure; that is, everything needed to transform the intermediate representation (IR) produced by frontends like Clang into assembly code and object files.You’ll learn how to write an optimizing code generator for a toy backend in LLVM. The chapters will guide you step by step through building this backend while exploring key concepts, such as the ABI, cost model, and register allocation. You’ll also find out how to express these concepts using LLVM's existing infrastructure and how established backends address these challenges. Furthermore, the book features code snippets that demonstrate the actual APIs.By the end of this book, you’ll have gained a deeper understanding of LLVM. The concepts presented are expected to remain stable across different LLVM versions, making this book a reliable quick reference guide for understanding LLVM.
Mayur Pandey, Suyog Sarda
The book is for compiler programmers who are familiar with concepts of compilers and want to indulge in understanding, exploring, and using LLVM infrastructure in a meaningful way in their work.This book is also for programmers who are not directly involved in compiler projects but are often involved in development phases where they write thousands of lines of code. With knowledge of how compilers work, they will be able to code in an optimal way and improve performance with clean code.
David Earl
You've scoured the forums, watched the tutorial videos, and done everything you can to learn the secrets of the art of making dance music. Everyone is saying something different about how to get into producing your own projects. This book will help connect the dots and lay a solid foundation of knowledge so you can get beats banging out of LMMS.This book will show you the ins and outs of making Dance music with LMMS. Do you make house, trance, techno or down-tempo? After this book you'll be able to make a song that stands out from the masses, using time honoured tricks of the trade. From inception to conception, this book will help give you a workflow to channel your muse using LMMS.Readers will be given a brief lesson on the best of dance music history, then learn how to recreate it using the Open Source digital workstation - LMMS. The reader will be guided through creating a project from start to finish. By the end of this book, the reader will know how to create a full dance track in LMMS and make it ready for distribution.Along the way, readers will take short stops into music theory, song arranging, recording, and other related information to give them a good foundation for making dance music with depth as well as power. Reading LMMS: A Complete Guide to Dance Music Production will not just teach the reader how to use LMMS, but also how good dance music is crafted. The reader will not just be taught how to make decisions in LMMS, but when and why. After devouring this book, the reader should be able to focus on his or her creativity, with LMMS as a co-conspirator in the process of making great dance music.
Samuel Sullins
Step into the world of low poly 3D art with Low Poly 3D Modeling in Blender—your entry point into Blender and mastering the fundamentals of 3D art. This beginner-friendly guide ensures that you’re fully prepared for the creative adventure that follows.Through a step-by-step learning process starting with the principles of low poly art, this book gradually immerses you in the intricacies of modeling. As you progress, you’ll gain hands-on experience creating diverse projects ranging from designing a simple 3D crate to rendering complete low poly scenes. The book covers a wide spectrum of topics as you navigate Blender's interface, mastering essential modeling tools and exploring both basic and advanced modeling techniques. Advancing to the final chapters, you’ll find ways to breathe life into your models with material creation and gain practical insights into modeling a variety of low poly objects. From end-to-end scene construction to configuring Blender for rendering high-quality images, you’ll be equipped with the foundational skills to propel your career in 3D modeling and explore the boundless creative possibilities that Blender offers.By the end of this book, you'll have a solid understanding of Blender, 3D modeling, low poly methodologies, material design, 3D rendering techniques, and the broader world of 3D art.
Lua Quick Start Guide. The easiest way to learn Lua programming
Gabor Szauer
Lua is a small, powerful and extendable scripting/programming language that can be used for learning to program, and writing games and applications, or as an embedded scripting language. There are many popular commercial projects that allow you to modify or extend them through Lua scripting, and this book will get you ready for that. This book is the easiest way to learn Lua. It introduces you to the basics of Lua and helps you to understand the problems it solves. You will work with the basic language features, the libraries Lua provides, and powerful topics such as object-oriented programming. Every aspect of programming in Lua, variables, data types, functions, tables, arrays and objects, is covered in sufficient detail for you to get started. You will also find out about Lua's module system and how to interface with the operating system.After reading this book, you will be ready to use Lua as a programming language to write code that can interface with the operating system, automate tasks, make playable games, and much more. This book is a solid starting point for those who want to learn Lua in order to move onto other technologies such as Love2D or Roblox.A quick start guide is a focused, shorter title that provides a faster paced introduction to a technology. It is designed for people who don't need all the details at this point in their learning curve. This presentation has been streamlined to concentrate on the things you really need to know.
Ciro Cardoso
This book offers uses practical applications using recipes with step-by-step instructions and useful information to help you master how to produce professional architectural visualizations in Lumion. The cookbook approach means you need to think and explore how a particular feature can be applied in your project and perform the intended task. This book is written to be accessible to all Lumion users and is a useful guide to follow when becoming familiar with this cutting-edge real-time technology.This practical guide is designed for all levels of Lumion users who know how to model buildings in 3D and a basic understanding of Lumion, who want to enhance their skills to the next level.
Ciro Cardoso
This book is designed for advanced Lumion users who wish to apply these techniques to their own projects. The reader should be familiar with modeling knowledge and have at least a basic knowledge of a graphics editing program, such as Photoshop or Gimp.
Witold Wrotek
Naucz się lutować jak profesjonalista Poznaj technikę lutowania od podstaw Wyposaż warsztat w odpowiednie narzędzia Naucz się łączyć różne elementy elektroniczne Nawet najbardziej zaawansowane układy elektryczne i elektroniczne nie mogłyby działać, gdyby ich elementy nie były ze sobą odpowiednio połączone. Najtańszą, najpopularniejszą i najskuteczniejszą metodą łączenia obwodów jest lutowanie. Zapewnia nie tylko właściwe połączenie elektryczne, lecz również odpowiednio wytrzymałe spojenie mechaniczne, a tym samym umożliwia stabilne mocowanie elementów na płytce drukowanej. I choć zalety lutowania jako techniki łączenia elementów metalowych znane są ludzkości od starożytności, to prawdziwą popularność zawdzięcza ono dopiero pojawieniu się współczesnej elektroniki, a wzrost znaczenia - jej gwałtownemu rozwojowi. Jeśli chcesz poznać technikę lutowania i nauczyć się prawidłowo stosować ją w praktyce, sięgnij po odpowiednie źródło wiedzy! Książka Lutowanie od podstaw krok po kroku wprowadzi Cię w tajniki sztuki łączenia elementów, przedstawi niezbędne narzędzia i dobre praktyki, nauczy unikać typowych błędów popełnianych przez początkujących oraz pokaże najlepsze sposoby lutowania różnych elementów elektrycznych i elektronicznych. Nauczysz się też dzięki niej, jak wykonać proste prace elektryczne w swoim domu, a nawet jak naprawić typowe usterki występujące w urządzeniach AGD. Przygotowanie do lutowania Niezbędne narzędzia i materiały Dobre praktyki i typowe błędy Praktyczne porady dotyczące lutowania Typowe prace i naprawy elektryczne Ćwiczenia poprawiające umiejętności Lutowanie elementów elektronicznych Usuwanie usterek elektronicznych Zostań prawdziwym mistrzem lutownicy!
Lutowanie od podstaw. Wydanie II
Witold Wrotek
Lutowanie - prosto, praktycznie, profesjonalnie! Opanuj podstawy techniki lutowania Poznaj przydatne narzędzia pracy Naucz się lutowania w praktyce Lutowanie to najtańsza, najpopularniejsza i najskuteczniejsza metoda łączenia elementów obwodów elektrycznych i elektronicznych. Nawet najbardziej zaawansowane układy nie mogłyby działać bez zastosowania tej techniki, która zapewnia nie tylko właściwe połączenie elektryczne, lecz również odpowiednio wytrzymałe spojenie mechaniczne, umożliwiając tym samym stabilne mocowanie elementów na płytce drukowanej. Do łączenia metalowych części lutowania używano już w starożytności, jednak prawdziwy rozkwit tej metody nastąpił wraz z gwałtownym rozwojem elektroniki. Sięgnij po odpowiednie źródło wiedzy, jeśli chcesz poznać technikę lutowania i nauczyć się prawidłowo stosować ją w praktyce! Drugie wydanie książki Lutowanie od podstaw wprowadzi Cię w tajniki sztuki łączenia elementów, przedstawi niezbędne narzędzia i dobre praktyki, nauczy unikać typowych błędów popełnianych przez początkujących, a także pokaże najlepsze sposoby lutowania różnych elementów elektrycznych i elektronicznych. Dzięki lekturze dowiesz się też, jak wykonać proste prace elektryczne w swoim domu, a nawet jak naprawić typowe usterki urządzeń AGD. Przygotowanie do lutowania Niezbędne narzędzia i materiały Dobre praktyki i najczęstsze błędy Praktyczne porady dotyczące lutowania Typowe prace i naprawy elektryczne Ćwiczenia poprawiające umiejętności Lutowanie elementów elektronicznych Usuwanie usterek elektronicznych Lutuj jak prawdziwy zawodowiec!
Lutowanie od podstaw. Wydanie III
Witold Wrotek
Wszystko, co musisz wiedzieć o lutowaniu Lutowanie jest procesem, w którym elementy metalowe łączy się trwale w wysokiej temperaturze za pomocą metalowego spoiwa zwanego lutem. To najtańsza, najpopularniejsza i najskuteczniejsza metoda łączenia elementów obwodów elektrycznych i elektronicznych. Nawet najbardziej zaawansowane układy nie mogłyby działać bez zastosowania tej techniki. Metodę tę ludzkość zna od starożytności, jednak lutowanie stało się szczególnie ważne, kiedy zaczęliśmy na szeroką skalę korzystać z elektroniki. Umiejętność lutowania drobnych elementów przydaje się zarówno zawodowcom, jak i majsterkowiczom hobbystom. Pozwala ona na samodzielny montaż i konserwowanie podzespołów, płytek drukowanych albo większych układów, na przykład tych, które wprawiają w ruch roboty. Jeśli planujesz zająć się elektroniką amatorsko lub zawodowo, ten poradnik jest skierowany właśnie do Ciebie. Dowiesz się z niego między innymi: Jak się przygotować do lutowania W jakie narzędzia i materiały trzeba się zaopatrzyć Na czym polegają dobre praktyki i najczęstsze błędy podczas lutowania Co warto wiedzieć przed przystąpieniem do procesu Jakie prace lutownicze wykonuje się najczęściej Co możesz zrobić, by poprawić precyzję swojej pracy W jaki sposób lutuje się elementy elektroniczne Jak usunąć najczęściej występujące usterki Trzecie wydanie książki zostało wzbogacone o dodatek poświęcony wyborowi odpowiedniej stacji lutowniczej.
Fabrizio Volpe, Alessio Giombini, Lasse N Wedo,...
If you work with Lync on a daily basis or if you have to use a specific feature of Lync for a project, this is the book for you. For solutions architects, technical consultants, and administrators, if you have a Lync deployment and you want to upgrade, integrate, secure, or extend it to the cloud, you can get valuable information from the recipes in this book.
Łamanie i zabezpieczanie aplikacji w systemie iOS
Jonathan Zdziarski
Twoja lektura obowiązkowa! iOS to obecnie jeden z najpopularniejszych systemów operacyjnych, wykorzystywany w urządzeniach firmy Apple. Jednak dzięki tej popularności jest on też łakomym kąskiem dla hakerów. Uzyskanie dostępu do danych przechowywanych w telefonie może mieć katastrofalne skutki. Dlatego jeżeli tworzysz aplikacje na platformę iOS, ta książka jest dla Ciebie pozycją obowiązkową. Jak obronić się przed atakiem? Wszystkie niezbędne informacje znajdziesz w tym wyjątkowym podręczniku. W trakcie lektury dowiesz się, jak działają hakerzy, jak wyszukują słabe punkty aplikacji oraz jak modyfikują jej kod. Ponadto nauczysz się utrudniać śledzenie kodu Twojej aplikacji oraz bezpiecznie usuwać pliki (tak, aby nie było możliwe ich odtworzenie). Wśród poruszanych tematów znajdziesz również te związane z transmisją danych: wykorzystanie protokołu SSL to nie wszystko, musisz zadbać także o to, żeby nie było możliwe przejęcie sesji SSL. Weź książkę do ręki i obroń się przed atakiem! Dzięki tej książce: zrozumiesz, jak działają hakerzy zabezpieczysz swoją aplikację przez nieuprawnionymi zmianami ochronisz swoje bezpieczne połączenia bezpowrotnie usuniesz niepotrzebne pliki zagwarantujesz bezpieczeństwo danych użytkownikom Twojej aplikacji Zadbaj o bezpieczeństwo danych użytkowników Twojej aplikacji!
Oliver Theobald
Starting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python.The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation.In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It’s a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.
Giuseppe Bonaccorso
Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Oliver Theobald
This book is an ideal starting point for anyone interested in Artificial Intelligence and Machine Learning. It begins with the foundational principles of AI, offering a deep dive into its history, building blocks, and the stages of development. Readers will explore key AI concepts and gradually transition to practical applications, starting with machine learning algorithms such as linear regression and k-nearest neighbors. Through step-by-step Python tutorials, the book helps readers build and implement models with hands-on experience.As the book progresses, readers will dive into advanced AI topics like deep learning, natural language processing (NLP), and generative AI. Topics such as recommender systems and computer vision demonstrate the real-world applications of AI technologies. Ethical considerations and privacy concerns are also addressed, providing insight into the societal impact of these technologies.By the end of the book, readers will have a solid understanding of both the theory and practice of AI and Machine Learning. The final chapters provide resources for continued learning, ensuring that readers can continue to grow their AI expertise beyond the book.
Uday Kamath, Krishna Choppella
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.
Andrew P. McMahon
The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized model factory for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Joshua Arvin Lat
Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required
Rodolfo Bonnin
Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.
Eduonix Learning Solutions
Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.
Revathi Gopalakrishnan, Avinash Venkateswarlu
Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples.You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains.By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Machine Learning for OpenCV. Intelligent image processing with Python
Michael Beyeler, Michael Beyeler (USD)
Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning.By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
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.
Stefan Jansen
The rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory.