Sztuczna inteligencja
Mercury Learning and Information, Oswald Campesato
This book bridges the gap between theoretical knowledge and practical application in Python programming, machine learning, and using ChatGPT-4 in data science. It starts with an introduction to Pandas for data manipulation and analysis. The book then explores various machine learning classifiers, from kNN to SVMs. Later chapters cover GPT-4's capabilities, enhancing linear regression analysis, and using ChatGPT in data visualization, including AI apps, GANs, and DALL-E.The journey begins with mastering Pandas and machine learning fundamentals. It progresses to applying GPT-4 in linear regression and machine learning classifiers. The final chapters focus on using ChatGPT for data visualization, making complex results accessible and understandable.Understanding these concepts is crucial for modern data scientists. This book transitions readers from basic Python programming to advanced applications of ChatGPT-4 in data science. Companion files with source code, datasets, and figures enhance learning, making this an essential resource for mastering Python, machine learning, and AI-driven data visualization.
Ashish Kumar, Joseph Babcock
Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python.You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books:1. Learning Predictive Analytics with Python2. Mastering Predictive Analytics with Python
Hassi Norlen
IBM Quantum Experience® is a leading platform for programming quantum computers and implementing quantum solutions directly on the cloud. This book will help you get up to speed with programming quantum computers and provide solutions to the most common problems and challenges.You’ll start with a high-level overview of IBM Quantum Experience® and Qiskit®, where you will perform the installation while writing some basic quantum programs. This introduction puts less emphasis on the theoretical framework and more emphasis on recent developments such as Shor’s algorithm and Grover’s algorithm. Next, you’ll delve into Qiskit®, a quantum information science toolkit, and its constituent packages such as Terra, Aer, Ignis, and Aqua. You’ll cover these packages in detail, exploring their benefits and use cases. Later, you’ll discover various quantum gates that Qiskit® offers and even deconstruct a quantum program with their help, before going on to compare Noisy Intermediate-Scale Quantum (NISQ) and Universal Fault-Tolerant quantum computing using simulators and actual hardware. Finally, you’ll explore quantum algorithms and understand how they differ from classical algorithms, along with learning how to use pre-packaged algorithms in Qiskit® Aqua.By the end of this quantum computing book, you’ll be able to build and execute your own quantum programs using IBM Quantum Experience® and Qiskit® with Python.
Jia Huang
Most developers can spin up a RAG pipeline in an afternoon using LangChain or LlamaIndex. Far fewer understand why retrieval fails or how to fix it. This book is for those who want to go deeper.'RAG From First Principles' dismantles the retrieval-augmented generation stack layer by layer, how documents are ingested and parsed, why chunking strategy directly impacts answer quality, how embedding models encode meaning, what happens inside a vector database, and how sparse and dense retrieval interact in a hybrid system. Written by Jia Huang, a research engineer and bestselling AI author, it brings research depth and production experience to one of AI's most critical engineering disciplines.Structured as a progressive dialogue between a seasoned engineer and two students, the book surfaces the questions practitioners actually ask. Each chapter builds on the last, from data import and chunking through embedding selection, index design, hybrid search, and post-retrieval processing, into response generation, evaluation, and advanced paradigms including GraphRAG, Agentic RAG, and Modular RAG.By the end, you'll have the architectural understanding to optimize, debug, and extend your RAG systems with confidence.
Denis Rothman
Stop moving your data to the AI. This second edition defines a revolutionary architectural shift: bringing the AI to the data. By using Oracle Database 23ai as a converged engine in this book, you will architect Sovereign AI systems that eliminate the fragmentation, latency, and massive security risks inherent in traditional data extraction.You’ll work with DualRAG, synchronizing unstructured vector semantics with the deterministic truth of structured SQL, Graph, and Spatial retrieval. This allows your systems to reason over verified corporate data rather than probabilistic guesses, reducing hallucinations at the source. Moving beyond simple pipelines, you’ll also build MAS-RAG (multi-agent systems for RAG), where autonomous agents coordinate across hybrid retrieval workflows, multimodal video pipelines, and graph-based knowledge structures.Designed for developers and architects, these blueprints transform disconnected data silos into a unified engine to architect autonomous enterprise intelligence that scales with RLHF and model fine-tuning. By the end of the book, you’ll be able to design and deploy enterprise AI systems that combine retrieval, reasoning, and structured data to build reliable generative AI applications.*Email sign-up and proof of purchase required
Rewolucja sztucznej inteligencji w medycynie. Jak GPT-4 może zmienić przyszłość
Peter Lee, Carey Goldberg, Isaac Kohane
Odkąd się pojawił, ChatGPT wzbudza ogromne zainteresowanie wśród ludzi różnych profesji. Szybko stało się jasne, że to jeden z największych przełomów technologicznych ostatnich kilkudziesięciu lat. Możemy oczekiwać, że modele językowe radykalnie wpłyną na to, jak ludzie będą pracować, komunikować się ze sobą i zdobywać wiedzę. Szczególnie dużo nadziei ― i obaw ― wiąże się z zastosowaniem GPT w medycynie. Niezależnie od tego, czy jesteś pracownikiem ochrony zdrowia, medykiem, czy pacjentem, powinieneś jak najszybciej zrozumieć możliwości tej technologii. W tej książce opisano różne zastosowania GPT-4 w medycynie: jako źródło informacji medycznej, wsparcie w podejmowaniu decyzji dotyczących leczenia czy też pomoc w tworzeniu dokumentacji, takiej jak podsumowanie wizyty pacjenta. Podczas lektury odkryjesz niezwykły potencjał tej technologii, przekonasz się także, jak bardzo może poprawić skuteczność diagnozowania i usprawnić niektóre procedury. Znajdziesz tu spostrzeżenia o potencjalnych wadach sztucznej inteligencji i najświeższe wnioski związane z jej zastosowaniem. Nie zabrakło również opisu zagrożeń związanych z tą technologią i wskazówek, do czego GPT nie można używać. Poszczególne zagadnienia zilustrowano prawdziwymi rozmowami z GPT-4. Są one w pełni spontaniczne i pozbawione poprawek, często błyskotliwe i czasami nietaktowne, wzbogacone o cenny kontekst i szczere komentarze. AI w medycynie: jutro zaczęło się wczoraj!
Paul Goodey GP, Paul Goodey
Do you want to be able to confidently design and build apps that support business processes within the Lightning Platform? Salesforce Platform App Builder Certification Guide not only helps you to do this, but also prepares you for the certification exam.The book starts by describing the core capabilities of the Lightning Platform. You'll learn techniques for data modeling to design, build, and deploy apps without writing code and achieve rapid results with the declarative capabilities that the Lightning Platform provides. Next, you'll explore utilities for importing and exporting data and the features available in the Lightning Platform to restrict and extend access to objects, fields, and records. You'll also be able to customize the Salesforce Lightning Experience user interface (UI) and build functionality for custom buttons, links, and actions. Later, this certification study guide will take you through reporting and the social and mobile features of the Lightning Platform. Finally, you’ll get to grips with Salesforce build environments and deployment options.By the end of this Salesforce book, you'll not only have learned how to build data models, enforce data security, and implement business logic and process automation, but also have gained the confidence to pass the Platform App Builder exam and achieve Salesforce certification.
Kamila Migdał-Najman, Krzysztof Najman
Prezentowana książka składa się z dwóch zasadniczych części. Część pierwsza ma charakter teoretyczny. Omawia genezę, rozwój, podstawy empiryczne i teoretyczne procesu klasyfikacji i grupowania danych. Jest pełna faktów, anegdot i własnych przemyśleń autorów. Część druga książki jest poświęcona szerokim badaniom teoretycznym, symulacyjnym i empirycznym nad własnościami samouczących się sieci neuronowych w grupowaniu danych społeczno-ekonomicznych. Szczegółowo omówiono algorytm budowy i samouczenia się trzech modeli sztucznych sieci neuronowych: SOM (Self Organizing Map), GNG (Growing Neural Gas) i sieci hybrydowej SOM-GNG. Zaproponowano także ich modyfikacje zwiększające zdolność badanych sieci do poprawnego wyróżniania istniejących skupień. W książce położono szczególny nacisk na możliwie prosty i przejrzysty opis często złożonych zjawisk. Poza koniecznym formalizmem matematycznym autorzy posługują się wieloma zaawansowanymi metodami wizualizacji omawianych zagadnień. Dzięki temu, mimo naukowego charakteru książki, może ona stanowić wartościowy podręcznik dla bardziej zaawansowanych studentów, praktyków i naukowców nie będących specjalistami w zakresie klasyfikacji i grupowania danych.