Python QuickStart Guide


Book Description

Learn Python fundamentals that can be used in any programming setting – use the guidance in this book to program your own game in a unique and practical Python learning experience. Learning Python opens the door to a world of programming possibilities. From AI and machine learning to video game, app, and web development, Python is a critical behind-the-scenes component of everyday technology. Python powers the services of household names like Google, Netflix, and Spotify along with tech pioneers like NASA, IBM, and Intel. Put simply, Python is the in-demand and easy-to-learn programming language that gets stuff done. In Python QuickStart Guide, senior developer and programmer Robert Oliver lays out the quickest and most accessible path yet to the mastery of Python fundamentals. Distilling his experience drawn from over two decades of working with Python and other programming languages, Robert’s clear voice and writing present a practical, hands-on approach that anyone, at any experience level, can use to become a Python programmer. It doesn’t matter if you are a new or existing programmer, a job seeker looking for a career change or promotion, or just someone who wants to learn how to automate basic tasks with Python—Robert’s step-by-step approach, complete with a hands-on companion Python game project, is the perfect starting point to master Python fundamentals! Python QuickStart Guide is Perfect for: - New or experienced programmers looking to enhance their career opportunities with an in-demand programming language - Job seekers who want to supercharge their resumes and increase their value in the job marketplace - Students or recent college grads who have their sights set on a lucrative position in the tech industry - Full stack developers or programmers who need to round out their programming skills to take on new projects - Coding or programming bootcamp students looking for supplemental learning material - Anyone who wants to explore the world of programming, use Python to automate tedious tasks, or enhance their resume and future-proof their skills! Python QuickStart Guide Explains - The best practical approach to learning Python—follow along with the exercises in the book to program your own video game and learn along the way - How to master Python building blocks and build a robust set of programming skills at your own pace - How to avoid common pitfalls new programmers face, how to debug code, and how to eliminate frustrating errors - Coding best practices that anyone can use to level up their programming skills using Python or any other programming language You Will Learn - How to Use Python – Practical Examples, Code Snippets, Plus Follow Along to Code Your Own Game! - Python Fundamentals – How to Use Python for Web Design and Interfacing with GitHub, SQL, and Other Applications - Object-Oriented Programming Principles – Managing Data, Scripts, Logic, Inputs, Outputs, and More! - Programming Essentials – Debugging, Producing Clean Code, Best Practices, Time-Savers, and Tips - Python Next Steps –Testing, Optimization, Speed Improvements, Integrations with Other Applications, and More! *Lifetime Access To Free Python Programming Digital Assets* In addition to the follow-along Python game included with the exercises in the book, Python QuickStart Guide comes with a library of references and cheat sheets to help you go beyond the book and get the most out of your Python learning experience. QuickStart Guides are books for beginners, written by experts. *Giving Back* QuickStart Guides plants trees in areas where they are needed most with the help of our reforestation partner One Tree Planted.




pytest Quick Start Guide


Book Description

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 ...




Quickstart Python


Book Description

Christoph Schäfer introduces the great world of programming with Python and provides a quick introduction to independent script development. He points out how the programming language Python has established itself in recent years alongside MATLAB and R as a standard at scientific workplaces in research and development, and shows that the great popularity of Python is based on its easy extensibility: It is very easy to use modules from other developers in your own scripts and programs. In particular, the author presents the modules NumPy, SciPy and Matplotlib, which offer scientists and engineers a perfect development environment for scientific and technical computing, for applications in physics, chemistry, biology and computer science. Python is also used in the latest applications in the highly topical fields of Big Data Science and Machine Learning. The author: Dr. Christoph Schäfer teaches and researches in the Department of Computational Physics at the Institute of Astronomy and Astrophysics at the Eberhard Karls University of Tübingen. This Springer essential is a translation of the original German 1st edition essentials, Schnellstart Python by Christoph Schäfer, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2019. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.




Ansible Quick Start Guide


Book Description

Configure Ansible and start coding YAML playbooks using the appropriate modules Key FeaturesCreate and use Ansible Playbook to script and organise management tasksBenefit from the Ansible community roles and modules to resolve complex and niche tasksWrite configuration management code to automate infrastructureBook Description Configuration Management (CM) tools help administrators reduce their workload. Ansible is one of the best Configuration Management tools, and can act as an orchestrator for managing other CMs. This book is the easiest way to learn how to use Ansible as an orchestrator and a Configuration Management tool. With this book, you will learn how to control and monitor computer and network infrastructures of any size,physical or virtual. You will begin by learning about the Ansible client-server architecture. To get started, you will set up and configure an Ansible server. You will then go through the major features of Ansible: Playbook and Inventory. Then, we will look at Ansible systems and network modules. You will then use Ansible to enable infrastructure automated configuration management, followed by best practices for using Ansible roles and community modules. Finally, you will explore Ansible features such as Ansible Vault, Ansible Containers, and Ansible plugins. What you will learnImplement Playbook YAML scripts and its capacities to simplify day-to-day tasksSetup Static and Dynamic InventoryUse Ansible predefined modules for Linux, Windows, networking, and virtualisation administrationOrganize and configure the host filesystem using storage and files modulesImplement Ansible to enable infrastructure automated configuration managementSimplify infrastructure administrationSearch and install new roles and enable them within AnsibleSecure your data using Ansible VaultWho this book is for This book is targeted at System Administrators and Network Administrators who want to use Ansible to automate an infrastructure. No knowledge of Ansible is required.




Pip System Corebook


Book Description




Quick Start Guide to Penetration Testing


Book Description

Get started with NMAP, OpenVAS, and Metasploit in this short book and understand how NMAP, OpenVAS, and Metasploit can be integrated with each other for greater flexibility and efficiency. You will begin by working with NMAP and ZENMAP and learning the basic scanning and enumeration process. After getting to know the differences between TCP and UDP scans, you will learn to fine tune your scans and efficiently use NMAP scripts. This will be followed by an introduction to OpenVAS vulnerability management system. You will then learn to configure OpenVAS and scan for and report vulnerabilities. The next chapter takes you on a detailed tour of Metasploit and its basic commands and configuration. You will then invoke NMAP and OpenVAS scans from Metasploit. Lastly, you will take a look at scanning services with Metasploit and get to know more about Meterpreter, an advanced, dynamically extensible payload that is extended over the network at runtime. The final part of the book concludes by pentesting a system in a real-world scenario, where you will apply the skills you have learnt. What You Will Learn Carry out basic scanning with NMAPInvoke NMAP from Python Use vulnerability scanning and reporting with OpenVAS Master common commands in Metasploit Who This Book Is For Readers new to penetration testing who would like to get a quick start on it.




Natural Language Processing with Python Quick Start Guide


Book Description

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.




Caffe2 Quick Start Guide


Book Description

Build and train scalable neural network models on various platforms by leveraging the power of Caffe2 Key FeaturesMigrate models trained with other deep learning frameworks on Caffe2Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devicesLeverage the distributed capabilities of Caffe2 to build models that scale easilyBook Description Caffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms. This book introduces you to the Caffe2 framework and shows how you can leverage its power to build, train, and deploy efficient neural network models at scale. It will cover the topics of installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. It will also show how to import models from Caffe and from other frameworks using the ONNX interchange format. It covers the topic of deep learning accelerators such as CPU and GPU and shows how to deploy Caffe2 models for inference on accelerators using inference engines. Caffe2 is built for deployment to a diverse set of hardware, using containers on the cloud and resource constrained hardware such as Raspberry Pi, which will be demonstrated. By the end of this book, you will be able to not only compose and train popular neural network models with Caffe2, but also be able to deploy them on accelerators, to the cloud and on resource constrained platforms such as mobile and embedded hardware. What you will learnBuild and install Caffe2Compose neural networksTrain neural network on CPU or GPUImport a neural network from CaffeImport deep learning models from other frameworksDeploy models on CPU or GPU accelerators using inference enginesDeploy models at the edge and in the cloudWho this book is for Data scientists and machine learning engineers who wish to create fast and scalable deep learning models in Caffe2 will find this book to be very useful. Some understanding of the basic machine learning concepts and prior exposure to programming languages like C++ and Python will be useful.




fastText Quick Start Guide


Book Description

Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book Description Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is for This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.




An Introduction to Statistical Genetic Data Analysis


Book Description

A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.