Natural Language Processing: Python and NLTK. Click here to enter text

Jacob Perkins, Nitin Hardeniya, Deepti Chopra, Iti Mathur, ...

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

Network Science with Python. Explore the networks around us using network science, social network analysis, and machine learning

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.

Okta Administration: Up and Running. Implement enterprise-grade identity and access management for on-premises and cloud apps

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.

OpenAI API Cookbook. Build intelligent applications including chatbots, virtual assistants, and content generators

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.

OpenCV 3 Blueprints. Expand your knowledge of computer vision by building amazing projects with OpenCV 3

Matthew John Macdonald, Jason Clemons, Gary Bradski, Alasdair McAndrew, ...

OpenCV Computer Vision Application Programming Cookbook. Over 50 recipes to help you build computer vision applications in C++ using the OpenCV library

Robert Laganiere

OpenCV for Secret Agents. Use OpenCV in six secret projects to augment your home, car, phone, eyesight, and any photo or drawing

Joseph Howse

OpenCV with Python By Example. Build real-world computer vision applications and develop cool demos using OpenCV for Python

Prateek Joshi

Practical Automated Machine Learning Using H2O.ai. Discover the power of automated machine learning, from experimentation through to deployment to production

Salil Ajgaonkar

With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities.You’ll begin by understanding how H2O’s AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you’ll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you’ll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you’ll take a hands-on approach to implementation using H2O that’ll enable you to set up your ML systems in no time.By the end of this H2O book, you’ll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science.

Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS

Emily Webber, Andrea Olgiati

Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization.With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you’ll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models.You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines.By the end of this book, you’ll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future.

Python: Advanced Predictive Analytics. Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

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

Quantum Computing in Practice with Qiskit(R) and IBM Quantum Experience(R). Practical recipes for quantum computer coding at the gate and algorithm level 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.

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!

Rozmowa z chatem GPT o przyszłości ludzi i świata

Reid Hoffman

To książka niezwykła. Dialog między człowiekiem a sztuczną inteligencją. Reid Hoffman przy współpracy GPT-4 zabiera czytelnika w podróż w świat przyszłości i zastanawia się, w jaki sposób sztuczna inteligencja, a w szczególności duże modele językowe, w tym GPT-4, mogą przyczynić się do rozwoju ludzkości w kluczowych obszarach, takich jak edukacja, biznes i kreatywność. Hoffman nie tylko pisze o GPT-4, lecz wchodzi z nim w interakcje, pozwalając czytelnikom zobaczyć możliwości technologii  zarówno jej mocne strony, jak i ograniczenia. Zapraszając GPT-4 do współpracy, Hoffman maluje intrygujący, wymagający i często zabawny obraz tego, z czym zapewne będziemy się mierzyć. To wyprawa do przyszłości, w której sztuczna inteligencja nie jest zagrożeniem, a partnerem. Wspólnikiem, który może pomóc człowiekowi rozwinąć skrzydła i uwolnić cały potencjał. Szanse i możliwości to nie wszystko. GPT-4 niesie ze sobą również wyzwania i wątpliwości. Książka próbuje odpowiedzieć na pytanie, w jaki sposób poradzić sobie z ryzykiem związanym z rozwojem technologii SI, które mogą przyspieszyć postęp ludzkości w czasach wymagających natychmiastowych rozwiązań na niespotykaną skalę. Autor rozpoczyna dialog i zaprasza nas do tej rozmowy. Reid Hoffman jest współzałożycielem portalu LinkedIn, współtwórcą spółki Inflection AI i partnerem w Greylock. Jest gospodarzem podcastów "Masters of Scale" i "Possible" oraz współautorem czterech bestsellerowych książek: Jesteś start-upem, The Alliance, Blitzscaling. Ścieżka błyskawicznej ekspansji firm oraz Mistrzowie skalowania biznesu.

Salesforce Platform App Builder Certification Guide. A beginner's guide to building apps on the Salesforce Platform and passing the Salesforce Platform App Builder exam

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.

Samouczące się sztuczne sieci neuronowe w grupowaniu i klasyfikacji danych. Teoria i zastosowania w ekonomii

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.

scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline

Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.

Solutions Architect's Handbook. Kick-start your solutions architect career by learning architecture design principles and strategies

Saurabh Shrivastava, Neelanjali Srivastav, Kamal Arora

Becoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect.This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect.By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.

Splunk 9.x Enterprise Certified Admin Guide. Ace the Splunk Enterprise Certified Admin exam with the help of this comprehensive prep guide

Srikanth Yarlagadda

The IT sector's appetite for Splunk and skilled Splunk developers continues to surge, offering more opportunities for developers with each passing decade. If you want to enhance your career as a Splunk Enterprise administrator, then Splunk 9.x Enterprise Certified Admin Guide will not only aid you in excelling on your exam but also pave the way for a successful career.You’ll begin with an overview of Splunk Enterprise, including installation, license management, user management, and forwarder management. Additionally, you’ll delve into indexes management, including the creation and management of indexes used to store data in Splunk. You’ll also uncover config files, which are used to configure various settings and components in Splunk.As you advance, you’ll explore data administration, including data inputs, which are used to collect data from various sources, such as log files, network protocols (TCP/UDP), APIs, and agentless inputs (HEC).You’ll also discover search-time and index-time field extraction, used to create reports and visualizations, and help make the data in Splunk more searchable and accessible. The self-assessment questions and answers at the end of each chapter will help you gauge your understanding.By the end of this book, you’ll be well versed in all the topics required to pass the Splunk Enterprise Admin exam and use Splunk features effectively.

Splunk Developer's Guide. Design, implement, and publish custom Splunk applications and add-ons following best practices

Kyle Smith

Splunk Operational Intelligence Cookbook. With Splunk, reporting and communicating insight is simple – find out with this Splunk book, created to help you unlock more effective Business Intelligence

Josh Diakun, Derek Mock, Paul R. Johnson

Sztuczna inteligencja 2041. 10 wizji przyszłości

Kai-Fu Lee, Chen Qiufan

Autor bestsellerowej książki Inteligencja sztuczna, rewolucja prawdziwa, ekspert do spraw AI, były prezes Google China, oraz znany powieściopisarz s.f. połączyli w tej książce siły, żeby odpowiedzieć na pytanie, jak sztuczna inteligencja zmieni nasz świat w ciągu najbliższych dwudziestu lat. Sztuczna inteligencja będzie definicją rozwoju XXI wieku wygeneruje bezprecedensowe bogactwo, zrewolucjonizuje medycynę i edukację poprzez symbiozę człowiek-maszyna oraz stworzy zupełnie nowe formy komunikacji i rozrywki. Jednak uwalniając nas od rutynowej pracy, zakwestionuje także zasady organizacyjne naszego ładu gospodarczego i społecznego i przyniesie nowe zagrożenia w postaci autonomicznej broni i inteligentnej technologii. W tym prowokacyjnym i oryginalnym dziele w dziesięciu porywających opowiadaniach, osadzonych w przyszłości, autorzy wprowadzają czytelników w szereg pouczających scenerii z 2041 roku.

Sztuczna inteligencja dla inżynierów. Istotne obszary i zastosowania

Mieczysław Muraszkiewicz, Robert Nowak

Na treść książki składają się przede wszystkim zagadnienia związane z zastosowaniami sztucznej inteligencji. Wstępem są rozważania na temat tzw. odpowiedzialnej sztucznej inteligencji. Podstawowym tworzywem, na którym działa sztuczna inteligencja, są dane, informacja i wiedza. O inżynierii wiedzy, a w tym o metodach reprezentacji wiedzy, traktuje rozdział drugi niniejszej monografii. Jego kontynuacją jest rozdział trzeci, omawiający typowe problemy i sposoby radzenia sobie z nimi, przy wykorzystywaniu uczenia maszynowego do analizy danych ustrukturyzowanych, zwanych też danymi tabelarycznymi. W rozdziale czwartym czytelnik znajdzie wykład na temat przetwarzania języka naturalnego. Rozdział piąty jest poświęcony bioinformatyce, czyli analizie napisów reprezentujących biopolimery DNA, RNA i białka. Do takich analiz z powodzeniem stosuje się metody sztucznej inteligencji. Innym obszarem jest analiza obrazu i dźwięku. Obecnie główną rolę odgrywają tutaj sztuczne sieci neuronowe. Zagadnienia te omówiono w rozdziale szóstym, w którym zawarto informacje dotyczące percepcji maszyn, w tym widzenia maszynowego oraz metod analizy dźwięku. Swego rodzaju nawiązaniem do rozdziału wstępnego jest rozdział siódmy, poświęcony wyjaśnialnej sztucznej inteligencji, która to kwestia ma zasadnicze znaczenie dla upowszechnienia systemów sztucznej inteligencji. Książkę zamyka rozdział na temat inżynierii uczenia maszynowego, który traktuje o poprawnym procesie realizacji projektów korzystających z metod uczenia maszynowego, a także obszerna bibliografia.