Sztuczna inteligencja
Paul Battisson
As applications built on the Salesforce platform are now a key part of many organizations, developers are shifting focus to Apex, Salesforce’s proprietary programming language. As a Salesforce developer, it is important to understand the range of tools at your disposal, how and when to use them, and best practices for working with Apex. Mastering Apex Programming will help you explore the advanced features of Apex programming and guide you in delivering robust solutions that scale.This book starts by taking you through common Apex mistakes, debugging, exception handling, and testing. You'll then discover different asynchronous Apex programming options and develop custom Apex REST web services. The book shows you how to define and utilize Batch Apex, Queueable Apex, and Scheduled Apex using common scenarios before teaching you how to define, publish, and consume platform events and RESTful endpoints with Apex. Finally, you'll learn how to profile and improve the performance of your Apex application, including architecture trade-offs.With code examples used to facilitate discussion throughout, by the end of the book, you'll have developed the skills needed to build robust and scalable applications in Apex.
Lior Gazit, Meysam Ghaffari
Natural Language Processing has evolved beyond rule-based systems and classical machine learning (ML). This second edition guides you through that transformation from mathematical and ML foundations to large language models, retrieval pipelines, agentic automation, and AI-native system design. It strengthens core NLP concepts while expanding into modern architectures such as transformers, parameter-efficient fine-tuning (LoRA and QLoRA), and alignment methods like RLHF and DPO.You’ll begin with essential linear algebra, probability, and ML principles before moving into text preprocessing, feature engineering, classification pipelines, and deep learning architectures. From there, the focus shifts to system design: building Retrieval-Augmented Generation (RAG) pipelines, implementing model routing strategies that balance cost and performance, and orchestrating structured multi-agent workflows. You'll also introduce structured interoperability patterns, including the Model Context Protocol (MCP). Governance and safety will be treated as architectural concerns, demonstrating how policy and compliance can be integrated directly into AI systems. By the end, you will have the tools to implement NLP techniques and be equipped to design, govern, and deploy intelligent systems built on them.*Email sign-up and proof of purchase required
Mastering QuickBooks 2020. The ultimate guide to bookkeeping and QuickBooks Online
Crystalynn Shelton
Intuit QuickBooks is an accounting software package that helps small business owners to manage all their bookkeeping tasks. Its complete range of accounting capabilities, such as tracking income and expenses, managing payroll, simplifying taxes, and accepting online payments, makes QuickBooks software a must-have for business owners and aspiring bookkeepers.This book is a handy guide to using QuickBooks Online to manage accounting tasks and drawing business insights by generating reports easily. Using a fictitious company, the book demonstrates how to create a QuickBooks Online account; customize key settings for a business; manage customers, vendors, and products and services; generate reports; and close the books at the end of the period. QuickBooks records your debits and credits, so you don't need to learn accounting. However, you will find out what happens under the hood and understand how your financial statements are impacted by what you do in QuickBooks. You'll also discover tips, shortcuts, and best practices that will help you save time and become a QuickBooks pro.By the end of this book, you'll have become well-versed with the features of QuickBooks and be able to set up your business and manage all bookkeeping tasks with ease.
Matematyczne modele uczenia maszynowego w językach MATLAB i PYTHON
Stanisław Osowski, Robert Szmurło
Prezentowane opracowanie dotyczy różnych modeli i metod stosowanych w uczeniu maszynowym. W szczególności, w poszczególnych rozdziałach przedstawione są takie zagadnienia, jak: regresja liniowa; klasyfikatory KNN; klasyfikatory Bayesa; modele matematyczne drzew decyzyjnych; sieci neuronowe MLP; sieci RBF; sieci SVM do klasyfikacji i regresji; sieci głębokie (CNN, autoenkoder, LSTM, transformer); zagadnienia zdolności generalizacyjnych modeli, w tym zespoły klasyfikatorów i systemów regresyjnych; transformacje i metody redukcji wymiaru danych wielowymiarowych; metody grupowania danych wielowymiarowych; wybrane metody generacji i selekcji cech diagnostycznych; metody oceny jakości rozwiązań; podstawowe rozwiązania adaptacyjnych systemów rozmytych. W przedstawieniu poszczególnych rozwiązań modelowych zaprezentowano zarówno strukturę pod-stawowych modeli, jak i algorytmy uczące dostosowane do konkretnego modelu. Ponieważ z punktu widzenia aktualnego stanu wiedzy do najważniejszych rozwiązań sztucznej inteligencji należą sztuczne sieci neuronowe. Tym zagadnieniom poświęcono najwięcej uwagi, wprowadzając różne rozwiązania sieciowe, w tym perceptron wielowarstwowy (MLP), sieć o radialnej funkcji bazowej (RBF), maszynę wektorów nośnych (SVM) czy różne rozwiązania głębokich sieci neuronowych wielowarstwowych, takich jak sieć konwolucyjna (CNN), autoenkoder (AE) czy sieć LSTM. Teoretyczne podstawy algorytmów uczących zostały zilustrowane przykładowymi programami implementującymi je przy użyciu oprogramowania Matlab i Python. Prezentowane w podręczniku skrypty z przykładami w Matlabie i Pythonie zostały udostępnione na platformie Github pod adresem: https://github.com/szmurlor/mmum. Podręcznik jest przeznaczony dla słuchaczy wyższych lat studiów, doktorantów i ludzi zainteresowanych metodami uczenia maszynowego, podstawowego narzędzia sztucznej inteligencji. Ze względu na interdyscyplinarny charakter tematyki może być wykorzystany zarówno w informatyce, inżynierii biomedycznej, jak i innych naukach technicznych. Wprowadzenie zarówno podstawowych jak i zaawansowanych pojęć uczenia maszynowego powoduje, że może być użyteczny dla osób początkujących i zaawansowanych w tej tematyce.
Hala Nelson
Sztuczna inteligencja i technologie oparte na danych są coraz częściej integrowane z istniejącymi systemami i operacjami. Ta tendencja dotyczy licznych branż. Dziś przy budowaniu systemów SI można korzystać z gotowych bibliotek, jeżeli jednak zależy Ci na w pełni świadomym tworzeniu doskonalszych aplikacji, musisz dobrze opanować matematykę leżącą u podstaw sztucznej inteligencji. Nawet jeśli nie darzysz królowej nauk płomiennym uczuciem, dzięki temu kompleksowemu opracowaniu z łatwością poradzisz sobie z jej lepszym poznaniem. Nie znajdziesz tu skomplikowanych teorii naukowych, tylko przystępnie podane koncepcje matematyczne niezbędne do rozwoju w dziedzinie sztucznej inteligencji, w szczególności do praktycznego stosowania najnowocześniejszych modeli. Poznasz takie zagadnienia jak regresja, sieci neuronowe, sieci konwolucyjne, optymalizacja, prawdopodobieństwo, procesy Markowa, równania różniczkowe i wiele innych w ekskluzywnym kontekście sztucznej inteligencji. Książkę docenią pasjonaci nowych technologii, twórcy aplikacji, inżynierowie i analitycy danych, a także matematycy i naukowcy. W książce: wyjaśnienie pojęć z zakresu uczenia maszynowego, inżynierii danych i matematyki ujednolicanie modeli w ramach jednej struktury matematycznej grafy i dane sieciowe eksploracja rzeczywistych danych, zmniejszanie liczby wymiarów i przetwarzanie obrazów korzystanie z modeli w różnych projektach opartych na danych implikacje i ograniczenia sztucznej inteligencji Ta książka w zachwycający sposób sprawia, że matematyka staje się zabawą dla licznych uczestników przyszłości opartej na sztucznej inteligencji! Adri Purkayastha, analityk oceny ryzyka, BNP Paribas O książce w mediach: Eksperyment Myślowy – recenzja książki
Aaron Guilmette, Steve Miles, Peter De Tender
The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud.This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You’ll explore Microsoft’s responsible AI principles like safety and accountability. Then, you’ll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you’ll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you’ll find chapter review questions with answers, provided as an online resource.By the end of this exam guide, you’ll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources.
Midjourney. Leverage The Future Of Creativity
Adrian Twarog
Unleash the full potential of Midjourney by mastering the essentials of prompt creation. The book begins with an in-depth exploration of prompt anatomy, teaching you the key components that make up an effective input. From there, you'll move on to crafting your first Midjourney prompt, gaining confidence as you bring your creative ideas to life.As you progress, you'll delve into the vital commands and the parameter list that enable you to fine-tune your results. Each concept is broken down into actionable insights, ensuring you fully understand how to control Midjourney’s output. By learning the interplay of these elements, you’ll start crafting outputs tailored to your vision.Finally, advanced techniques take center stage, showing you how to push the boundaries of your creativity. Whether you're creating stunning visuals, refining complex ideas, or exploring new ways to express yourself, this course equips you with the tools and knowledge to excel in the world of Midjourney.
Mit sztucznej inteligencji. Dlaczego komputery nie potrafią myśleć jak my
Erik J. Larson
Czy superinteligencja naprawdę jest tuż za rogiem? Czy sztuczna inteligencja wkrótce przewyższy możliwości ludzkiego umysłu? Czy jesteśmy na drodze do stworzenia świadomych, rozumujących maszyn? Erik Larson, przedsiębiorca z branży technologicznej i pionier badań nad przetwarzaniem języka naturalnego, zabiera czytelnika w podróż po współczesnym krajobrazie AI, pokazując, jak daleko wciąż jesteśmy od stworzenia superinteligencji i co musiałoby się wydarzyć, by to osiągnąć. Od czasów Alana Turinga wielu entuzjastów AI utożsamia sztuczną inteligencję z ludzkim myśleniem. To poważne nieporozumienie. Obecne systemy AI bazują na rozumowaniu indukcyjnym - analizują dane i przewidują wyniki. Tymczasem ludzie nie porównują danych - formułują przypuszczenia, czerpiąc z kontekstu i doświadczenia. Nasza inteligencja to sieć domysłów opartych na wiedzy o świecie. Tego typu intuicyjnego rozumowania - nazywanego abdukcją - nie potrafimy jak dotąd zaprogramować. A to właśnie ono stanowi sedno ludzkiego rozumu i zdrowego rozsądku. Larson ostrzega, że mit nieuchronnej superinteligencji nie tylko zaciemnia obraz rzeczywistego postępu, ale też hamuje innowacje. Zamiast badać to, czego jeszcze nie rozumiemy, skupiamy się na przereklamowanych rozwiązaniach. AI będzie dalej doskonalić się w wyspecjalizowanych zadaniach, ale jeśli naprawdę chcemy zrobić krok naprzód, musimy lepiej poznać i docenić jedyną inteligencję, którą znamy - naszą własną. Erik J. Larson jest informatykiem, przedsiębiorcą w branży technologicznej i założycielem dwóch start-upów AI finansowanych przez DARPA. Obecnie zajmuje się podstawowymi zagadnieniami przetwarzania języka naturalnego i uczenia maszynowego. Publikował na łamach "The Atlantic" oraz w czasopismach naukowych, a także badał techniczne granice sztucznej inteligencji w ramach współpracy z inkubatorem technologicznym IC2 na Uniwersytecie Teksańskim w Austin.
Mehul Gupta, Niladri Sen
This book offers a detailed introduction to the groundbreaking field of AI agents and Model Context Protocol (MCP). The first section delves into generative AI and large language models (LLMs), exploring how these technologies power modern AI systems. From there, the book introduces the concept of AI agents—autonomous systems capable of executing tasks with varying levels of complexity. Moving into practical applications, the book focuses on Model Context Protocol, explaining its key components and how it enables effective interaction between AI and various software tools. Each chapter offers step-by-step instructions for setting up MCP servers for popular tools like Gmail, YouTube, GitHub, and more, empowering readers to automate tasks and streamline workflows. The book concludes by addressing the future of MCP, its potential risks, and how to stay safe while using these advanced technologies. Whether you're a beginner or experienced practitioner, this guide will deepen your understanding of AI and enhance your ability to leverage cutting-edge automation in daily operations.
V Kishore Ayyadevara, Yeshwanth Reddy
Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production.By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.
Valentina Alto
Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data.Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster.You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications.By the end of this book, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.
Mercury Learning and Information, Oswald Campesato
This book introduces developers to basic concepts in NLP and machine learning, providing numerous code samples to support the topics covered. The journey begins with introductory material on NumPy and Pandas, essential for data manipulation. Following this, chapters delve into NLP concepts, algorithms, and toolkits, providing a solid foundation in natural language processing.As you progress, the book covers machine learning fundamentals and classifiers, demonstrating how these techniques are applied in NLP. Practical examples using TF2 and Keras illustrate how to implement various NLP tasks. Advanced topics include the Transformer architecture, BERT-based models, and the GPT family of models, showcasing the latest advancements in the field.The final chapters and appendices offer a comprehensive overview of related topics, including data and statistics, Python3, regular expressions, and data visualization with Matplotlib and Seaborn. Companion files with source code and figures ensure a hands-on learning experience. This book equips you with the knowledge and tools needed to excel in NLP and machine learning.
Mercury Learning and Information, Oswald Campesato
This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to those with varied technical backgrounds, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.
Natural Language Processing: Python and NLTK. Click here to enter text
Jacob Perkins, Nitin Hardeniya, Deepti Chopra, Iti...
Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it’s becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:? NTLK essentials by Nitin Hardeniya? Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins? Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur
Mercury Learning and Information, Oswald Campesato
This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to a technical audience, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.
David Knickerbocker
Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You’ll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You’ll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you’ll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You’ll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you’ll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.
Neuro-Symbolic AI. Design transparent and trustworthy systems that understand the world as you do
Alexiei Dingli, David Farrugia
Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches.You’ll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you’ll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You’ll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI.Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions.
(Nie)etyczna AI. Jak programować odpowiedzialnie w erze sztucznej inteligencji
Paweł Półtorak
Technologia - wspaniała i groźna zarazem. Także w Twoich rękach Czy rozwój sztucznej inteligencji budzi w Tobie mieszane uczucia? Z jednej strony fascynuje potencjałem, z drugiej - rodzi wątpliwości? Autor książki, doświadczony strateg i doradca w obszarze nowych technologii, stawia się w pozycji sygnalisty, by zwrócić uwagę na obszary, w których rozwój AI może pójść w niebezpiecznym kierunku. Choć dostrzega ogromne możliwości, jakie niesie sztuczna inteligencja - od transformacji biznesów po poprawę jakości życia - nie odwraca wzroku od ciemnych stron tej technologii. Znajdziesz tu analizę ryzyka związanego z AI, takiego jak brak transparentności, niewłaściwe zarządzanie danymi i potencjalne manipulacje, które mogą wpływać na społeczeństwo w skali globalnej. Celem książki nie jest jedynie wywołanie refleksji nad tymi zagrożeniami, ale przede wszystkim pokazanie, jak im zapobiegać. W pierwszej części autor zwraca uwagę na niebezpieczeństwa, jakie wiążą się z nieumiejętnym, nieprzemyślanym, a czasami także nieetycznym podejściem do projektowania technologii i zastosowań AI. W drugiej proponuje zestaw praktycznych wskazówek dla każdego, kto pracuje nad rozwojem sztucznej inteligencji i pragnie robić to w sposób etyczny. Ta książka to nie tylko ostrzeżenie przed zagrożeniami związanymi z AI, ale przede wszystkim drogowskaz dla twórców nowoczesnych technologii.
Lovisa Stenbäcken Stjernlöf, HenkJan de Vries
IAM, short for identity and access management, is a set of policies and technologies for ensuring the security of an organization through careful role and access assignment for users and devices. With this book, you’ll get up and running with Okta, an identity and access management (IAM) service that you can use for both employees and customers.Once you’ve understood how Okta can be used as an IAM platform, you’ll learn about the Universal Directory, which covers how to integrate other directories and applications and set up groups and policies. As you make progress, the book explores Okta’s single sign-on (SSO) feature and multifactor authentication (MFA) solutions. Finally, you will delve into API access management and discover how you can leverage Advanced Server Access for your cloud servers and Okta Access Gateway for your on-premises applications.By the end of this Okta book, you’ll have learned how to implement Okta to enhance your organization's security and be able to use this book as a reference guide for the Okta certification exam.
Henry Habib, Sam McKay, Paul Siegel
As artificial intelligence continues to reshape industries with OpenAI at the forefront of AI research, knowing how to create innovative applications such as chatbots, virtual assistants, content generators, and productivity enhancers is a game-changer. This book takes a practical, recipe-based approach to unlocking the power of OpenAI API to build high-performance intelligent applications in diverse industries and seamlessly integrate ChatGPT in your workflows to increase productivity.You’ll begin with the OpenAI API fundamentals, covering setup, authentication, and key parameters, and quickly progress to the different elements of the OpenAI API. Once you’ve learned how to use it effectively and tweak parameters for better results, you’ll follow advanced recipes for enhancing user experience and refining outputs. The book guides your transition from development to live application deployment, setting up the API for public use and application backend. Further, you’ll discover step-by-step recipes for building knowledge-based assistants and multi-model applications tailored to your specific needs.By the end of this book, you’ll have worked through recipes involving various OpenAI API endpoints and built a variety of intelligent applications, ready to apply this experience to building AI-powered solutions of your own.
Henry Habib
Firma OpenAI pozostaje niekwestionowanym liderem badań nad sztuczną inteligencją. Dzięki udostępnianym przez nią rozwiązaniom tworzenie innowacyjnych aplikacji, takich jak czatboty, wirtualne asystenty, generatory treści i narzędzia zwiększające produktywność, stało się o wiele prostsze. Bezsprzecznie powszechna dostępność technologii AI zmienia zasady gry! Receptury zawarte w tym zbiorze ułatwią Ci budowę szerokiej gamy inteligentnych aplikacji. Zaczniesz od podstaw OpenAI API - konfiguracji, uwierzytelniania i kluczowych parametrów - po czym szybko przejdziesz do nauki korzystania z najważniejszych elementów API. Następnie przyjdzie czas na zaawansowane receptury, dzięki którym poprawisz wrażenia użytkownika i dopracujesz dane wyjściowe. Dowiesz się, jak wdrażać aplikacje i przygotować je do publicznego użytku. Nauczysz się również budowania inteligentnych asystentów opartych na specjalistycznej wiedzy, a także aplikacji multimodalnych dostosowanych do Twoich specyficznych potrzeb. W książce: podstawy interfejsu OpenAI API, jego możliwości i ograniczenia konfiguracja OpenAI API krok po kroku zaawansowane funkcje, w tym komunikat systemowy i dostrajanie integracja OpenAI API z istniejącymi aplikacjami i procesami projektowanie aplikacji wykorzystujących w pełni możliwości OpenAI API Postęp technologii polega na dostosowaniu jej tak, abyśmy nawet jej nie zauważali i by mogła stać się częścią codziennego życia! Bill Gates
Joseph Howse, Steven Puttemans, Utkarsh Sinha
Computer vision is becoming accessible to a large audience of software developers who can leverage mature libraries such as OpenCV. However, as they move beyond their first experiments in computer vision, developers may struggle to ensure that their solutions are sufficiently well optimized, well trained, robust, and adaptive in real-world conditions. With sufficient knowledge of OpenCV, these developers will have enough confidence to go about creating projects in the field of computer vision.This book will help you tackle increasingly challenging computer vision problems that you may face in your careers. It makes use of OpenCV 3 to work around some interesting projects. Inside these pages, you will find practical and innovative approaches that are battle-tested in the authors’ industry experience and research. Each chapter covers the theory and practice of multiple complementary approaches so that you will be able to choose wisely in your future projects. You will also gain insights into the architecture and algorithms that underpin OpenCV’s functionality.We begin by taking a critical look at inputs in order to decide which kinds of light, cameras, lenses, and image formats are best suited to a given purpose. We proceed to consider the finer aspects of computational photography as we build an automated camera to assist nature photographers. You will gain a deep understanding of some of the most widely applicable and reliable techniques in object detection, feature selection, tracking, and even biometric recognition. We will also build Android projects in which we explore the complexities of camera motion: first in panoramic image stitching and then in video stabilization.By the end of the book, you will have a much richer understanding of imaging, motion, machine learning, and the architecture of computer vision libraries and applications!
Robert Laganiere
OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Joseph Howse
This book is for programmers who want to expand their skills by building fun, smart, and useful systems with OpenCV. The projects are ideal in helping you to think creatively about the uses of computer vision, natural user interfaces, and ubiquitous computers (in your home, car, and hand).