LiveCode Mobile Development Cookbook. 90 practical recipes for creating cross-platform mobile applications with the power of LiveCode

Dr. Edward Lavieri

LLM Engineer's Handbook. Master the art of engineering large language models from concept to production

Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir, ...

Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.

LLM Prompt Engineering for Developers. The Art and Science of Unlocking LLMs' True Potential

Aymen El Amri

LLM Prompt Engineering For Developers begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering.As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology.This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.

LLM w projektowaniu oprogramowania. Tworzenie inteligentnych aplikacji i agentów z wykorzystaniem dużych modeli językowych

Valentina Alto

Duże modele językowe (LLM) stały się technologicznym przełomem. Ich wszechstronność i funkcjonalność sprawiły, że coraz częściej mówi się o nowej erze inteligentnie działających urządzeń i aplikacji. Umiejętność zastosowania LLM we własnych projektach już dziś jest koniecznością dla wielu projektantów i programistów. Dzięki tej książce opanujesz podstawowe koncepcje związane z użyciem LLM. Poznasz unikatowe cechy i mocne strony kilku najważniejszych modeli (w tym GPT, Gemini, Falcon). Następnie dowiesz się, w jaki sposób LangChain, lekki framework Pythona, pozwala na projektowanie inteligentnych agentów do przetwarzania danych o nieuporządkowanej strukturze. Znajdziesz tu również informacje dotyczące dużych modeli podstawowych, które wykraczają poza obsługę języka i potrafią wykonywać różne zadania związane na przykład z grafiką i dźwiękiem. Na koniec zgłębisz zagadnienia dotyczące ryzyka związanego z LLM, a także poznasz techniki uniemożliwiania tym modelom potencjalnie szkodliwych działań w aplikacji. Najciekawsze zagadnienia: architektura dużych modeli językowych unikatowe funkcje LLM komponenty służące do koordynacji sztucznej inteligencji, w tym tworzenia frontendu użycie wiedzy nieparametrycznej i wektorowych baz danych dostrajanie dużych modeli językowych do własnych potrzeb odpowiedzialność i etyka w systemach korzystających z LLM Odkryj, jak łatwo model generatywnej AI zintegruje się z Twoją aplikacją!

LLVM Cookbook. Over 80 engaging recipes that will help you build a compiler frontend, optimizer, and code generator using LLVM

Mayur Pandey, Suyog Sarda

LLVM Essentials. Become familiar with the LLVM infrastructure and start using LLVM libraries to design a compiler

Mayur Pandey, Suyog Sarda, David Farago

LLVM is currently the point of interest for many firms, and has a very active open source community. It provides us with a compiler infrastructure that can be used to write a compiler for a language. It provides us with a set of reusable libraries that can be used to optimize code, and a target-independent code generator to generate code for different backends. It also provides us with a lot of other utility tools that can be easily integrated into compiler projects.This book details how you can use the LLVM compiler infrastructure libraries effectively, and will enable you to design your own custom compiler with LLVM in a snap.We start with the basics, where you’ll get to know all about LLVM. We then cover how you can use LLVM library calls to emit intermediate representation (IR) of simple and complex high-level language paradigms. Moving on, we show you how to implement optimizations at different levels, write an optimization pass, generate code that is independent of a target, and then map the code generated to a backend. The book also walks you through CLANG, IR to IR transformations, advanced IR block transformations, and target machines. By the end of this book, you’ll be able to easily utilize the LLVM libraries in your own projects.

LLVM Techniques, Tips, and Best Practices Clang and Middle-End Libraries. Design powerful and reliable compilers using the latest libraries and tools from LLVM

Min-Yih Hsu

Every programmer or engineer, at some point in their career, works with compilers to optimize their applications. Compilers convert a high-level programming language into low-level machine-executable code. LLVM provides the infrastructure, reusable libraries, and tools needed for developers to build their own compilers. With LLVM’s extensive set of tooling, you can effectively generate code for different backends as well as optimize them.In this book, you’ll explore the LLVM compiler infrastructure and understand how to use it to solve different problems. You’ll start by looking at the structure and design philosophy of important components of LLVM and gradually move on to using Clang libraries to build tools that help you analyze high-level source code. As you advance, the book will show you how to process LLVM IR – a powerful way to transform and optimize the source program for various purposes. Equipped with this knowledge, you’ll be able to leverage LLVM and Clang to create a wide range of useful programming language tools, including compilers, interpreters, IDEs, and source code analyzers.By the end of this LLVM book, you’ll have developed the skills to create powerful tools using the LLVM framework to overcome different real-world challenges.

Low-Code Application Development with Appian. The practitioner's guide to high-speed business automation at enterprise scale using Appian

Stefan Helzle

This book is an exhaustive overview of how the Appian Low-Code BPM Suite enables tech-savvy professionals to rapidly automate business processes across their organization, integrating people, software bots, and data. This is crucial as 80% of all software development is expected to be carried out in low code by 2024.This practical guide helps you master business application development with Appian as a beginner low-code developer. You'll learn to automate business processes using Appian low-code, records, processes, and expressions quickly and on an enterprise scale. In a fictional development project, guided by step-by-step explanations of the concepts and practical examples, this book will empower you to transform complex business processes into software.At first, you’ll learn the power of no-code with Appian Quick Apps to solve some of your most crucial business challenges. You’ll then get to grips with the building blocks of an Appian, starting with no-code and advancing to low-code, eventually transforming complex business requirements into a working enterprise-ready application.By the end of this book, you'll be able to deploy Appian Quick Apps in minutes and successfully transform a complex business process into low-code process models, data, and UIs to deploy full-featured, enterprise-ready, process-driven, mobile-enabled apps.

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.

L÷VE for Lua Game Programming. If you want to create 2D games for Windows, Linux, and OS X, this guide to the L?ñVE framework is a must. Written for hobbyists and professionals, it will help you leverage Lua for fast and easy game development

Darmie Akinlaja, Damilare Akinlaja

L?ñVE is a game development framework for making 2D games using the Lua programming language. L?ñVE is totally free, and can be used in anything from friendly open-source hobby projects, to closed-source commercial ones. Using the Lua programming framework, one can use L?ñVE2D to make any sort of interesting games.L?ñVE for Lua Game Programming will quickly and efficiently guide you through how to develop a video game from idea to prototype. Even if you are new to game programming, with this book, you will soon be able to create as many game titles as you wish without stress.The L?ñVE framework is the quickest and easiest way to build fully-functional 2D video games. It leverages the Lua programming language, which is known to be one of the easiest game development languages to learn and use. With this book, you will master how to develop multi-platform games for Windows, Linux, and Mac OS X. After downloading and installing L?ñVE, you will learn by example how to draw 2D objects, animate characters using sprites, and how to create game physics and game world maps.L?ñVE for Lua Game Programming makes it easier and quicker for you to learn everything you need to know about game programming. If you're interested in game programming, then this book is exactly what you've been looking for.

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!

Mac Application Development by Example: Beginner's Guide. A comprehensive and practical guide, for absolute beginners, to developing your own App for Mac OS X book and

Robert Wiebe

It's never been more important to have the ability to develop an App for Mac OS X. Whether it's a System Preference, a business app that accesses information in the Cloud, or an application that uses multi-touch or uses a camera, you will have a solid foundation in app development to get the job done.Mac Application Development by Example takes you through all the aspects of using the Xcode development tool to produce complete working apps that cover a broad range of topics. This comprehensive book on developing applications covers everything a beginner needs to know and demonstrates the concepts using examples that take advantage of some of the most interesting hardware and software features available.You will discover the fundamental aspects of OS X development while investigating innovative platform features to create a final product which take advantage of the unique aspects of OS X.Learn how to use Xcode tools to create and share Mac OS X apps. Explore numerous OS X features including iCloud, multi-touch trackpad, and the iSight camera.This book provides you with an illustrated and annotated guide to bring your idea to life using fundamental concepts that work on Mac.

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Giuseppe Bonaccorso

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition

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.

Machine Learning and Generative AI for Marketing. Take your data-driven marketing strategies to the next level using Python

Yoon Hyup Hwang, Nicholas C. Burtch

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience.This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standardsBy the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.

Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python

Dario Radečić

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.

Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples

Andrew McMahon

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

Machine Learning for Algorithmic Trading. Predictive models to extract signals from market and alternative data for systematic trading strategies with Python - Second Edition

Stefan Jansen

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

Emmanuel Tsukerman

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.

Machine Learning for Developers. Uplift your regular applications with the power of statistics, analytics, and machine learning

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.

Machine Learning for Mobile. Practical guide to building intelligent mobile applications powered by machine learning

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 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition

Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler

OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.

Machine Learning for OpenCV. Intelligent image processing with Python

Michael Beyeler

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!

Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Ben Auffarth

The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.