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.
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.
Brian Sacash, Bhargav Srinivasa-Desikan, Reddy Anil Kumar
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
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
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.
Krishna Dayanidhi
This book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial.
Richard M. Reese
Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon Web Services (AWS). You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentence, or semantic word.