Python

Python
Author: Toby Donaldson
Publisher: Peachpit Press
Total Pages: 288
Release: 2008-12-04
Genre: Computers
ISBN: 0132104121

Python is a remarkably powerful dynamic programming language that is used in a wide variety of application domains such as Web, database access, desktop GUIs, game and software development, and network programming. Fans of Python use the phrase "batteries included" to describe the standard library, which covers everything from asynchronous processing to zip files. The language itself is a flexible powerhouse that can handle practically any application domain. This task-based tutorial is for students with no programming experience as well as those programmers who have some experience with the programming language and now want to take their skills to the next level. The book walks a reader through all the fundamentals and then moves on to more advanced topics. It's a complete end-to-end tutorial and reference.

Natural Language Processing with Python Quick Start Guide

Natural Language Processing with Python Quick Start Guide
Author: Nirant Kasliwal
Publisher: Packt Publishing Ltd
Total Pages: 177
Release: 2018-11-30
Genre: Computers
ISBN: 1788994108

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description 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 workflow 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. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Python

Python
Author: Toby Donaldson
Publisher: Pearson Education
Total Pages: 225
Release: 2014
Genre: Computers
ISBN: 0321929551

Python is a remarkably powerful dynamic programming language used in a wide variety of situations such as Web, database access, desktop GUIs, game and software development, and network programming. Fans of Python use the phrase "batteries included" to describe the standard library, which covers everything from asynchronous processing to zip files. The language itself is a flexible powerhouse that can handle practically any application domain. This task-based tutorial on Python is for those new to the language and walks you through the fundamentals. You'll learn about arithmetic, strings, and variables; writing programs; flow of control, functions; strings; data structures; input and output; and exception handling. At the end of the book, a special section walks you through a longer, realistic application, tying the concepts of the book together.

Python

Python
Author: Chris Fehily
Publisher: Peachpit Press
Total Pages: 444
Release: 2002
Genre: Computers
ISBN: 9780201748840

Named after the Monty Python comedy troupe, Python is an interpreted, open-source, object-oriented programming language. It's also free and runs portably on Windows, Mac OS, Unix, and other operating systems. Python can be used for all manner of programming tasks, from CGI scripts to full-fledged applications. It is gaining popularity among programmers in part because it is easier to read (and hence, debug) than most other programming languages, and it's generally simpler to install, learn, and use. Its line structure forces consistent indentation. Its syntax and semantics make it suitable for simple scripts and large programs. Its flexible data structures and dynamic typing allow you to get a lot done in a few lines. To learn it, you'll need is some basic programming experience and a copy of Python: Visual QuickStart Guide. In patented Visual QuickStart Guide fashion, the book doesn't just tell you how to use Python to develop applications, it shows you, breaking Python into easy-to-digest, step-by-step tasks and providing example code. Python: Visual QuickStart Guide emphasizes the core language and libraries, which are the building blocks for programs. Author Chris Fehily starts with the basics - expressions, statements, numbers, strings - then moves on to lists, dictionaries, functions, and modules before wrapping things up with straightforward discussions of exceptions and classes. Some additional topics covered include: - Object-oriented programming- Working in multiple operating systems- Structuring large programs- Comparing Python to C, Perl, and Java- Handling errors gracefully.

The Quick Python Book

The Quick Python Book
Author: Vernon L. Ceder
Publisher: Manning Publications Company
Total Pages: 336
Release: 2010
Genre: Computers
ISBN: 9781935182207

Introduces the programming language's syntax, control flow, and basic data structures and covers its interaction with applications and mangement of large collections of code.

The Python Quick Syntax Reference

The Python Quick Syntax Reference
Author: Gregory Walters
Publisher: Apress
Total Pages: 140
Release: 2014-02-28
Genre: Computers
ISBN: 1430264799

The Python Quick Syntax Reference is the "go to" book that contains an easy to read and use guide to Python programming and development. This condensed code and syntax reference presents the Python language in a well-organized format designed to be used time and again. You won't find jargon, bloated samples, case studies, or history of Hello World and computer theory in this handy reference. This Python syntax reference is packed with useful information and is a must-have for any Python developer.

pytest Quick Start Guide

pytest Quick Start Guide
Author: Bruno Oliveira
Publisher: Packt Publishing Ltd
Total Pages: 153
Release: 2018-08-29
Genre: Computers
ISBN: 1789343828

Python's built-in unittest module is showing it's age; hard to extend, debug and track what's going on. The pytest framework overcomes these problems and simplifies testing your Python software. Many users love to use pytest and the improvement in their testing shows! This book is the ideal introduction to pytest, teaching you how to write ...

Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide
Author: Simeon Kostadinov
Publisher: Packt Publishing Ltd
Total Pages: 115
Release: 2018-11-30
Genre: Computers
ISBN: 1789133661

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.

Learning Python

Learning Python
Author: Mark Lutz
Publisher: "O'Reilly Media, Inc."
Total Pages: 1740
Release: 2013-06-12
Genre: Computers
ISBN: 1449355692

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide
Author: Edward L. Platt
Publisher: Packt Publishing Ltd
Total Pages: 181
Release: 2019-04-26
Genre: Computers
ISBN: 1789950414

Manipulate and analyze network data with the power of Python and NetworkX Key FeaturesUnderstand the terminology and basic concepts of network scienceLeverage the power of Python and NetworkX to represent data as a networkApply common techniques for working with network data of varying sizesBook Description NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems. What you will learnUse Python and NetworkX to analyze the properties of individuals and relationshipsEncode data in network nodes and edges using NetworkXManipulate, store, and summarize data in network nodes and edgesVisualize a network using circular, directed and shell layoutsFind out how simulating behavior on networks can give insights into real-world problemsUnderstand the ongoing impact of network science on society, and its ethical considerationsWho this book is for If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.