Big data
Gökhan Ozar
Any database designer who wants to accomplish both everyday tasks and more advanced actions with a few clicks or drag-and-drops can now do so using Navicat's advanced tools and this book.Starting with the basics before progressing with advanced features, this book can be read from cover to cover, or simply used as a reference guide for any problems you encounter.The book features 'work along' tutorials, some of which will surprise you by revealing features of Navicat which you may never have known existed ñ features such as designing functions and stored procedures, event triggers, creating batch jobs and scheduling.MySQL Management and Administration with Navicat is an ideal resource to master Navicat and unlock its true potential.
Myślenie statystyczne. Jak analizować dane i wydobywać z nich wiedzę. Wydanie III
Allen B. Downey
Dla większości z nas statystyka jest poddziedziną matematyki związaną z opracowywaniem teoretycznych podstaw prawdopodobieństwa i wnioskowania statystycznego. Analitycy danych podchodzą do tego inaczej: dla nich statystyka jest niezbędnym zestawem narzędzi i praktyk, które służą do pracy z danymi, odpowiadania na pytania i ułatwiają podejmowanie najlepszych decyzji. To trzecie wydanie przewodnika cenionego przez analityków danych, inżynierów oprogramowania i pasjonatów danologii. Dzięki niemu szybko nauczysz się korzystać z bibliotek NumPy, SciPy i Pandas. Poznasz różne metody eksploracji i wizualizacji danych, odkrywania zależności i trendów, a także prezentowania wyników. Struktura książki odpowiada rzeczywistemu procesowi pracy ze zbiorem danych: od importowania i oczyszczenia, przez analizę wieloczynnikową, aż po wizualizację uzyskanych wyników. Wszystkie rozdziały są dostępne w formie notatników Jupytera, dzięki czemu możesz jednocześnie czytać tekst, uruchamiać kod i pracować nad ćwiczeniami. W książce znajdziesz również takie zagadnienia jak: analiza rozkładów danych i wizualizacja wzorców za pomocą bibliotek Pythona korzystanie z modeli regresji analiza szeregów czasowych i analiza przeżycia tworzenie zrozumiałych wizualizacji danych rozwiązywanie typowych problemów związanych z analizą danych Jeśli chcesz się szybko nauczyć statystyki i stosowania jej w praktyce, to ta książka jest dla Ciebie! Zachary del Rosario, adiunkt w Olin College of Engineering
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.
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.
Richard M. Reese , Richard M Reese
If you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.
Cuantum Technologies LLC
Embark on a comprehensive journey to master natural language processing (NLP) with Python. Begin with foundational concepts like text preprocessing, tokenization, and key Python libraries such as NLTK, spaCy, and TextBlob. Explore the challenges of text data and gain hands-on experience in cleaning, tokenizing, and building basic NLP pipelines. Early chapters provide practical exercises to solidify your understanding of essential techniques.Advance to sophisticated topics like feature engineering using Bag of Words, TF-IDF, and embeddings like Word2Vec and BERT. Delve into language modeling with RNNs, syntax parsing, and sentiment analysis, learning to apply these techniques in real-world scenarios. Chapters on topic modeling and text summarization equip you to extract insights from data, while transformer-based models like BERT take your skills to the next level. Each concept is paired with Python-based examples, ensuring practical mastery.The final chapters focus on real-world projects, such as developing chatbots, sentiment analysis dashboards, and news aggregators. These hands-on applications challenge you to design, train, and deploy robust NLP solutions. With its structured approach and practical focus, this book equips you to confidently tackle real-world NLP challenges and innovate in the field.
Thushan Ganegedara
Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.