Big data
Mercury Learning and Information, Roger W. Pryor
This updated edition of the book explores COMSOL 5 and MATLAB, essential modeling tools for engineers and scientists. It includes five new models and covers systems from 0D to 3D, introducing numerical analysis techniques in COMSOL 5.6 and MATLAB. Using examples from electromagnetic, electronic, optical, thermal physics, and biomedical models, the book provides fundamental concepts and step-by-step instructions for building each model. Companion files include all models and related animations.The course starts with modeling methodology and material properties, progressing through 0D electrical circuit interface, 1D, 2D, 2D axisymmetric, 2D simple and complex mixed mode, and 3D modeling. Advanced topics like Perfectly Matched Layer models and Bioheat models are also covered. Each chapter builds on the previous one, ensuring a comprehensive understanding of modeling techniques.Understanding these concepts is crucial for developing and analyzing engineering, science, and biomedical systems. This book transitions readers from basic to advanced modeling, combining theoretical knowledge with practical skills. Companion files enhance the learning experience, making this an essential resource for mastering COMSOL 5 and MATLAB.
MySQL 8 Administrator???s Guide. Effective guide to administering high-performance MySQL 8 solutions
Chintan Mehta, Hetal Oza, Subhash Shah, Ravi...
MySQL is one of the most popular and widely used relational databases in the world today. The recently released version 8.0 brings along some major advancements in the way your MySQL solution can be administered. This handbook will be your companion to understand the newly introduced features in MySQL and show you how you can leverage them to design a high-performance MySQL solution for your organization.This book starts with a brief introduction to the new features in MySQL 8, and then quickly jumping onto the crucial administration topics that you will find useful in your day-to-day work. Topics such as migrating to MySQL 8, MySQL benchmarking, achieving high performance by implementing the indexing techniques, and optimizing your queries are covered in this book. You will also learn how to perform replication, scale your MySQL solution and implement effective security techniques. There is also a special section on the common and not so common troubleshooting techniques for effective MySQL administration is also covered in this book. By the end of this highly practical book, you will have all the knowledge you need to tackle any problem you might encounter while administering your MySQL solution.
MySQL 8 Cookbook. Over 150 recipes for high-performance database querying and administration
Karthik Appigatla
MySQL is one of the most popular and widely used relational databases in the World today. The recently released MySQL 8 version promises to be better and more efficient than ever before.This book contains everything you need to know to be the go-to person in your organization when it comes to MySQL. Starting with a quick installation and configuration of your MySQL instance, the book quickly jumps into the querying aspects of MySQL. It shows you the newest improvements in MySQL 8 and gives you hands-on experience in managing high-transaction and real-time datasets. If you've already worked with MySQL before and are looking to migrate your application to MySQL 8, this book will also show you how to do that. The book also contains recipes on efficient MySQL administration, with tips on effective user management, data recovery, security, database monitoring, performance tuning, troubleshooting, and more.With quick solutions to common and not-so-common problems you might encounter while working with MySQL 8, the book contains practical tips and tricks to give you the edge over others in designing, developing, and administering your database effectively.
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.
Thushan Ganegedara
Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures.The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Deborah A. Dahl
Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future.By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Manpreet Singh Ghotra, Rajdeep Dua
If you're aware of the buzz surrounding the terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
James Loy
Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch.By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
V Kishore Ayyadevara
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.