Uczenie maszynowe

Beginning Swift. Master the fundamentals of programming in Swift 4

Rob Kerr, Kare Morstol

Take your first foray into programming for Apple devices with Swift.Swift is fundamentally different from Objective-C, as it is a protocol-oriented language. While you can still write normal object-oriented code in Swift, it requires a new way of thinking to take advantage of its powerful features and a solid understanding of the basics to become productive.

Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!

David Stephenson

Koncepcja big data zmieniła zasady gry w biznesie. Wiele osób z kadry zarządczej nie rozumie specyfiki tego rodzaju danych: ogromnych, szybko narastających, często niepasujących do tradycyjnej struktury. Są one zasadniczo różne od konwencjonalnych danych, zarówno pod względem wielkości, jak i złożoności. Rzucają nowe wyzwania, stwarzają nowe możliwości, zacierają tradycyjne granice konkurencji i zmuszają do zmiany paradygmatów pozyskiwania wartości z danych. Big data i data science wraz z uczeniem maszynowym radykalnie zmieniają ekosystem biznesu. Aby przetrwać tę rewolucję, trzeba dostosować się do nowych warunków. Ta książka jest przystępnym wprowadzeniem do koncepcji big data i data science. Pozwoli na uzyskanie wiedzy niezbędnej do oceny, czy korzyści z tych technologii są warte kosztów i wysiłku związanych z wdrożeniem w firmie. Poszczególne techniki zostały dokładnie i przejrzyście opisane. Przedstawiono zasady tworzenia odpowiednich strategii. Wyjaśniono, jakich zasobów i jakich ludzi potrzeba do przeprowadzenia transformacji w kierunku zbierania, analizy i wykorzystywania danych, a także omówiono związane z tym ryzyko. Ważnym elementem książki są praktyczne wskazówki i podpowiedzi. W tej książce: podstawy big data, data science i sztucznej inteligencji praktyczne zastosowanie big data w technikach analitycznych przegląd podstawowych rodzajów analityki i dobór technologii przygotowanie firmy do wdrożenia projektów big data i data science wymagania prawne i ochrona danych a korzystanie z narzędzi big data Big data: łatwiejsze, niż myślisz, skuteczniejsze, niż marzysz!

Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R

Michele Usuelli

A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems.The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system.

Building Computer Vision Projects with OpenCV 4 and C++. Implement complex computer vision algorithms and explore deep learning and face detection

David Millán Escrivá, Prateek Joshi, Vinícius G....

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:•Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá•Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi

Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models

Dan Meador

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills.In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works.By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.

Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition

Luis Pedro Coelho, Willi Richert , Matthieu...

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

Building Natural Language and LLM Pipelines. Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph

Laura Funderburk

Modern LLM applications often break in production due to brittle pipelines, loose tool definitions, and noisy context. This book shows you how to build production-ready, context-aware systems using Haystack and LangGraph. You’ll learn to design deterministic pipelines with strict tool contracts and deploy them as microservices. Through structured context engineering, you’ll orchestrate reliable agent workflows and move beyond simple prompt-based interactions. You'll start by understanding LLM behavior—tokens, embeddings, and transformer models—and see how prompt engineering has evolved into a full context engineering discipline. Then, you'll build retrieval-augmented generation (RAG) pipelines with retrievers, rankers, and custom components using Haystack’s graph-based architecture. You’ll also create knowledge graphs, synthesize unstructured data, and evaluate system behavior using Ragas and Weights & Biases. In LangGraph, you’ll orchestrate agents with supervisor-worker patterns, typed state machines, retries, fallbacks, and safety guardrails. By the end of the book, you’ll have the skills to design scalable, testable LLM pipelines and multi-agent systems that remain robust as the AI ecosystem evolves.*Email sign-up and proof of purchase required

Building Smart Drones with ESP8266 and Arduino. Build exciting drones by leveraging the capabilities of Arduino and ESP8266

Syed Omar Faruk Towaha

With the use of drones, DIY projects have taken off. Programmers are rapidly moving from traditional application programming to developing exciting multi-utility projects.This book will teach you to build industry-level drones with Arduino and ESP8266 and their modified versions of hardware.With this book, you will explore techniques for leveraging the tiny WiFi chip to enhance your drone and control it over a mobile phone. This book will start with teaching you how to solve problems while building your own WiFi controlled Arduino based drone. You will also learn how to build a Quadcopter and a mission critical drone. Moving on you will learn how to build a prototype drone that will be given a mission to complete which it will do it itself. You will also learn to build various exciting projects such as gliding and racing drones. By the end of this book you will learn how to maintain and troubleshoot your drone.By the end of this book, you will have learned to build drones using ESP8266 and Arduino and leverage their functionalities to the fullest.