Programowanie
Niezależnie czy dopiero rozpoczynacie swoją przygodę z programowaniem, czy jesteście już uznanymi na rynku profesjonalistami, to w kategorii Programowanie na pewno znajdziecie podręczniki, które pomogą Wam w przebiegu pracy, czy też w nauce podstaw programowania.
W książkach z tego działu zawarta jest wiedza zarówno związana z czysto technicznymi sprawami typu składnia języków, ale także z umiejętnościami bardziej "miękkimi" jak obsługa i wykorzystanie pełnych możliwości środowisk programistycznych, czy też projektowanie oprogramowania lub metody numeryczne czy oraz struktury danych.
NativeScript for Angular Mobile Development. Creating dynamic mobile apps for iOS and Android
Nathan Walker, Nathanael J. Anderson
NativeScript is an open source framework that is built by Progress in order to build truly native mobile apps with TypeScript, JavaScript or just Angular which is an open source framework built by Google that offers declarative templates, dependency injection, and fully featured modules to build rich applications. Angular’s versatile view handling architecture allows your views to be rendered as highly performant UI components native to iOS and Android mobile platforms. This decoupling of the view rendering layer in Angular combined with the power of native APIs with NativeScript have together created the powerful and exciting technology stack of NativeScript for Angular.This book focuses on the key concepts that you will need to know to build a NativeScript for Angular mobile app for iOS and Android. We’ll build a fun multitrack recording studio app, touching on powerful key concepts from both technologies that you may need to know when you start building an app of your own. The structure of the book takes the reader from a void to a deployed app on both the App Store and Google Play, serving as a reference guide and valuable tips/tricks handbook.By the end of this book, you’ll know majority of key concepts needed to build a successful NativeScript for Angular app.
Natura kodu. Symulowanie systemów naturalnych przy użyciu JavaScript
Daniel Shiffman
Co by było, gdyby za pomocą kodu można było odtworzyć budzące podziw wzory stada ptaków lub hipnotyczny taniec świetlików? Książka Natura kodu już od ponad dekady umożliwia to niezliczonym czytelnikom, wypełniając lukę między twórczą ekspresją a programowaniem. Ten innowacyjny przewodnik autorstwa Daniela Shiffmana, twórcy uwielbianego Coding Train, zaprasza zarówno początkujących, jak i doświadczonych programistów do świata, w którym kod spotyka się z radosną kreatywnością. To wydanie przełomowego dzieła Shiffmana, oparte na JavaScripcie, powoli odkrywa tajemnice świata przyrody, zamieniając złożone tematy, takie jak algorytmy genetyczne, symulacje oparte na fizyce i sieci neuronowe, w przystępne i oszałamiające wizualnie kreacje. Praca Shiffmana przekształciła tysiące dociekliwych umysłów w twórców, przełamując bariery między nauką, sztuką i technologią oraz zachęcając Czytelników do postrzegania kodu nie tylko jako narzędzia do wykonywania zadań, ale jako płótna dla nieograniczonej kreatywności. Niezależnie od tego, czy rozszyfrowujesz eleganckie wzorce zjawisk naturalnych, czy też tworzysz własne cyfrowe ekosystemy, wskazówki Shiffmana z pewnością dostarczą informacji i inspiracji. W Naturze kodu nie chodzi tylko o kodowanie, lecz o nowe spojrzenie na świat przyrody i sprawienie, aby jego cuda zainspirowały twoje następne dzieła. Zanurz się i odkryj radość z przekształcania kodu w sztukę - a to wszystko przy jednoczesnym opanowaniu podstaw kodowania. Rozpocznij tę niezwykłą przygodę z projektami obejmującymi: Mechanizmy fizyki: symuluj przyciąganie grawitacyjne. Stado ptaków: twórz choreografię hipnotyzującego tańca stada. Rozgałęziające się drzewa: twórz realistyczne i organiczne struktury drzew. Sieci neuronowe: twórz inteligentne systemy, które uczą się i adaptują. Automaty komórkowe: odkrywaj magię samoorganizujących się wzorców. Algorytmy ewolucyjne: bądź świadkiem naturalnej selekcji we własnym kodzie. O autorze Daniel Shiffman, twórca kanału The Coding Train na YouTube (www.youtube.com/c/TheCodingTrain), większość swojego wolnego czasu poświęca, ucząc kodowania prawie 2 miliony subskrybentów, poprzez połączenie szczegółowych samouczków i filmów z projektami. Jako profesor sztuki w NYU Tisch School of the Arts i współzałożyciel Processing Foundation, Shiffman ma misję umożliwienia dociekliwym uczniom i ludziom na całym świecie wyrażania siebie poprzez kod.
Bhargav Srinivasa-Desikan
Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
Sohom Ghosh, Dwight Gunning
If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
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.
Mona M, Premkumar Rangarajan, Julien Simon
Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic.Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Mona M, Premkumar Rangarajan, Julien Simon
Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic.Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Tadej Magajna
Flair is an easy-to-understand natural language processing (NLP) framework designed to facilitate training and distribution of state-of-the-art NLP models for named entity recognition, part-of-speech tagging, and text classification. Flair is also a text embedding library for combining different types of embeddings, such as document embeddings, Transformer embeddings, and the proposed Flair embeddings.Natural Language Processing with Flair takes a hands-on approach to explaining and solving real-world NLP problems. You'll begin by installing Flair and learning about the basic NLP concepts and terminology. You will explore Flair's extensive features, such as sequence tagging, text classification, and word embeddings, through practical exercises. As you advance, you will train your own sequence labeling and text classification models and learn how to use hyperparameter tuning in order to choose the right training parameters. You will learn about the idea behind one-shot and few-shot learning through a novel text classification technique TARS. Finally, you will solve several real-world NLP problems through hands-on exercises, as well as learn how to deploy Flair models to production.By the end of this Flair book, you'll have developed a thorough understanding of typical NLP problems and you’ll be able to solve them with Flair.
