Sztuczna inteligencja
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.