Modern Python Cookbook. 133 recipes to develop flawless and expressive programs in Python 3.8 - Second Edition

Steven F. Lott

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.

Modern Python Standard Library Cookbook. Over 100 recipes to fully leverage the features of the standard library in Python

Alessandro Molina

The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python.The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data.By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs.

Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition

Manu Joseph, Jeffrey Tackes, Christoph Bergmeir

Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

Modernizing Oracle Tuxedo Applications with Python. A practical guide to using Oracle Tuxedo in the 21st century

Aivars Kalvans

Despite being developed in the 1980s, Oracle Tuxedo still runs a significant part of critical infrastructure and is not going away any time soon. Modernizing Oracle Tuxedo Applications with Python will help you get to grips with the most important Tuxedo concepts by writing Python code.The book starts with an introduction to Oracle Tuxedo and guides you in installing its latest version and Python bindings for Tuxedo on Linux. You'll then learn how to build your first server and client, configure Tuxedo, and start running an application. As you advance, you'll understand load balancing and work with the BBL server, which is at the heart of a Tuxedo application. This Tuxedo book will also cover Boolean expressions and different ways to export Tuxedo buffers for storage and transmission, before showing you how to implement servers and clients and use the management information base to change the configuration dynamically. Once you've learned how to configure Tuxedo for transactions and control them in application code, you'll discover how to use the store-and-forward functionality to reach destinations and use an Oracle database from a Tuxedo application.By the end of this Oracle Tuxedo book, you'll be able to perform common Tuxedo programming tasks with Python and integrate Tuxedo applications with other parts of modern infrastructure.

MySQL for Python. Integrating MySQL and Python can bring a whole new level of productivity to your applications. This practical tutorial shows you how with examples and explanations that clarify even the most difficult concepts

Albert Lukaszewski

Python is a dynamic programming language, which is completely enterprise ready, owing largely to the variety of support modules that are available to extend its capabilities. In order to build productive and feature-rich Python applications, we need to use MySQL for Python, a module that provides database support to our applications. Although you might be familiar with accessing data in MySQL, here you will learn how to access data through MySQL for Python efficiently and effectively.This book demonstrates how to boost the productivity of your Python applications by integrating them with the MySQL database server, the world's most powerful open source database. It will teach you to access the data on your MySQL database server easily with Python's library for MySQL using a practical, hands-on approach. Leaving theory to the classroom, this book uses real-world code to solve real-world problems with real-world solutions.The book starts by exploring the various means of installing MySQL for Python on different platforms and how to use simple database querying techniques to improve your programs. It then takes you through data insertion, data retrieval, and error-handling techniques to create robust programs. The book also covers automation of both database and user creation, and administration of access controls. As the book progresses, you will learn to use many more advanced features of Python for MySQL that facilitate effective administration of your database through Python. Every chapter is illustrated with a project that you can deploy in your own situation.By the end of this book, you will know several techniques for interfacing your Python applications with MySQL effectively so that powerful database management through Python becomes easy to achieve and easy to maintain.

Myśl w języku Python! Nauka programowania. Wydanie II

Allen B. Downey

Aby stać się cenionym programistą, trzeba zacząć od bardzo solidnych podstaw. Python jest idealną propozycją dla osób, które chcą nauczyć się programowania. Składnia i podstawowe koncepcje programistyczne w Pythonie są dość proste do zrozumienia. Sam język ma duże możliwości zastosowania w różnych dziedzinach wiedzy. Umożliwia przy tym pisanie czytelnego i łatwego w konserwacji kodu, co jest ogromną zaletą. Trzymasz w ręku praktyczny przewodnik do nauki programowania. Znajdziesz w nim przystępnie napisane wyjaśnienia dotyczące podstawowych pojęć programistycznych. Dowiesz się, jak stosować funkcje, czym jest rekurencja, jak wyglądają struktury danych i na czym polega projektowanie obiektowe. W każdym rozdziale znalazły się praktyczne ćwiczenia, dzięki którym będziesz używać poznawanych koncepcji i utrwalisz zdobytą wiedzę. W tej książce: przedstawiono podstawy Pythona, w tym jego składnię i semantykę opisano najważniejsze koncepcje programistyczne i zdefiniowano istotne pojęcia pokazano, jak stosować wartości, zmienne, instrukcje, funkcje i struktury danych przedstawiono metody pracy z plikami i bazami danych wyjaśniono zagadnienia programowania obiektowego opisano techniki debugowania służące do usuwania błędów składniowych, uruchomieniowych i semantycznych Python: dzięki niemu zaczniesz myśleć jak informatyk!

Myśl w języku Python! Nauka programowania. Wydanie III

Allen B. Downey

Python to wspaniały język programowania. Jest wszechstronny, wyrazisty i zwięzły, pozwala też korzystać z rosnącej kolekcji narzędzi i bibliotek. Cenią go zarówno profesjonalni twórcy oprogramowania, jak i amatorzy czy osoby spoza branży, które w Pythonie widzą cenne narzędzie do tworzenia własnych aplikacji, znacząco poprawiających jakość i wydajność pracy. Wyjątkowy przewodnik dla osób zainteresowanych nauką programowania od podstaw! Luciano Ramalho, autor książki Zaawansowany Python To trzecie wydanie przejrzystego przewodnika, który ułatwi Ci naukę programowania w Pythonie. Zaczniesz od przyswojenia podstawowych pojęć programistycznych, aby wkrótce płynnie posługiwać się funkcjami i strukturami danych. Zdobędziesz też umiejętność programowania zorientowanego obiektowo. W tym zaktualizowanym wydaniu znajdziesz również wskazówki, dzięki którym zastosujesz duże modele językowe, takie jak ChatGPT, do nauki programowania. Dowiesz się, jak tworzyć skuteczne zapytania dla tych modeli, a także jak testować i debugować kod Pythona. Dzięki ćwiczeniom, zamieszczonym w każdym rozdziale, będziesz stopniowo szlifować umiejętności programistyczne, a zasugerowane w książce strategie pomogą Ci w unikaniu frustrujących błędów - w ten sposób szybko nauczysz się tworzyć poprawny kod. W książce: podstawy Pythona zmienne, instrukcje, funkcje i struktury danych praca z plikami i bazami danych obiekty, metody i programowanie zorientowane obiektowo obsługa błędów składniowych, wykonawczych i semantycznych użycie dużych modeli językowych do przyspieszenia nauki programowania Dzięki tej książce nauczysz się używać dużych modeli językowych do nauki programowania! Sam Lau, współautor książki Learning Data Science   O książce: Eksperyment myślowy — recenzja książki  

Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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.

Natural Language Processing Fundamentals. Build intelligent applications that can interpret the human language to deliver impactful results

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.

Natural Language Processing with AWS AI Services. Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

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.

Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair

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.

Natural Language Processing with Python. Master text processing, language modeling, and NLP applications with Python's powerful tools

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.

Natural Language Processing with Python Quick Start Guide. Going from a Python developer to an effective Natural Language Processing Engineer

Nirant Kasliwal

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.

Natural Language Processing with TensorFlow. Teach language to machines using Python's deep learning library

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.

Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition

Thushan Ganegedara, Andrei Lopatenko

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.

Nauka programowania. Rusz głową!

Eric Freeman

Przewodnik po kodowaniu i myśleniu komputacyjnym Programista to bardzo szczególny typ specjalisty. Jeśli uważasz, że myśli w inny sposób niż tak zwani normalni ludzie, to masz rację. Dobra wiadomość jest taka, że i Ty możesz się nauczyć myślenia komputacyjnego - umiejętności, która się przydaje niezależnie od charakteru rozwiązywanego problemu, środowiska czy języka programowania. Tylko w ten sposób można od początku nauki programowania pisać przejrzysty, uporządkowany, znakomity kod, zgodny z najlepszymi praktykami wypracowanymi przez mistrzów. Innymi słowy: pracować jak profesjonalny programista. Ta książka jest niezwykłym podręcznikiem programowania. Być może wygląda nieco dziwacznie, ale prędko się przekonasz, że to podręcznik jest wyjątkowo skuteczny: w końcu jego formuła została opracowana na podstawie najlepszych osiągnięć neurologii i kognitywistyki. W ten sposób Twój mózg się zaangażuje i błyskawicznie przyswoi sobie zasady programowania w Pythonie. Autor wykorzystał oczywistą prawdę, że najszybciej uczymy się wtedy, gdy uwzględnimy specyfikę działania własnego mózgu! Najpierw więc się zainteresujesz, potem zaangażujesz, wreszcie przygotujesz sobie warsztat pracy, czyli zainstalujesz Pythona. Później zaczniesz ćwiczyć myślenie komputacyjne i oczywiście napiszesz swój pierwszy program. A dalej będzie coraz ciekawiej... W tej książce między innymi: Istotne koncepcje programistyczne Zasady programowania w Pythonie Funkcje i rekurencja Programowanie obiektowe Tworzenie API dla aplikacji internetowych Widgety i zdarzenia Neurony płoną. Emocje szaleją. Tak napiszesz kod godny mistrza!