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Designing Microservices with Django
Designing Microservices with Django

Explore microservices using the Python-based Django framework and review the benefits and drawbacks of them. This book will examine what microservices look like, how they talk to each other, and how they are crafted using the Python programming language and the Django web framework. You'll start by understanding what the key differences are between microservices and monolithic architectures. The book then does a deep dive into how microservices are built and what common models have emerged in our industry. You'll also take an extensive look at communication and ownership patterns and examine methodologies to speed up your architecture evolution by writing less but more distributed code using the Python programming language and the Django web framework. By the end of the book, you'll have a solid understanding of microservices architectures. Armed with a comprehensive and solid toolset, you can begin working toward systems that are more scalable, resilient, and maintainable. ...
Beginning Kubernetes on the Google Cloud Platform
Beginning Kubernetes on the Google Cloud Platform

Use this beginner's guide to understand and work with Kubernetes on the Google Cloud Platform and go from single monolithic Pods (the smallest unit deployed and managed by Kubernetes) all the way up to distributed, fault-tolerant stateful backing stores. You need only a familiarity with Linux, Bash, and Python to successfully use this book. Proficiency in Docker or cloud technology is not required. You will follow a learn-by-doing approach, running small experiments and observing the effects. Google open sourced Kubernetes in 2015 and now it is the industry standard in container orchestration. It has been adopted by all leading vendors of cloud, on-prem, and hybrid infrastructure services: Microsoft (Azure AKS), Amazon (AWS EKS), IBM (IBM Cloud Kubernetes Services), Alibaba Cloud (ACK), RedHat (OpenShift), and Pivotal (PKS). Even though Kubernetes is offered by all of the market-leading cloud providers, the Google Cloud Platform (GCP) offers an integrated shell (Google Cloud Shell) ...
Raspberry Pi Projects for Kids
Raspberry Pi Projects for Kids

The Raspberry Pi is an inexpensive, pocket-sized computer that will help you build and code your own hardware projects. Raspberry Pi Projects for Kids will show you how to harness the power of the Raspberry Pi to create 12 cool projects using simple code and common materials like a webcam, microphone, and LED lights. Step-by-step instructions and detailed diagrams guide you through each project. After a brief introduction to the Python programming language, you'll learn how to: Create an LED night-light that turns itself on and off; Set up a Raspberry Pi camera to take selfies and videos; Set up a webcam to stream video to your cell phone; Manipulate environments in Minecraft; Hijack local radio waves to play your own songs and recordings; Configure Raspberry Pi to send texts to a cell phone; Track your family members locations via wi-fi and Bluetooth; Create an MP3 player; Set up a camera to take motion-triggered photos of wildlife; Control the electronics in your home with your ...
Mining Social Media
Mining Social Media

Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web; Download data archives and dig through them for insights; Inspect HTML downloaded from websites for useful content; Format, aggregate, sort, and filter your collected data using Google Sheets; Create data visualizations to illustrate your discoveries; Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library; Apply what you've learned to research topics on you ...
Learn TensorFlow 2.0
Learn TensorFlow 2.0

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. Review the new features of TensorFlow 2.0; Use TensorFlow 2.0 to build machine learning and deep learning models; Perform sequence prediction ...
Object Design Style Guide
Object Design Style Guide

Objects are the central concept of languages like Java, Python, C#. Applying best practices for object design means that your code will be easy to read, write, and maintain. Object Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP. Well-written OO code is a pleasure to read, modify, and debug. Elevate your coding style by mastering the universal best practices for object design presented in this book. These clearly presented rules, which apply to any OO language, maximize the clarity and durability of your codebase and increase productivity for you and your team. Object Design Style Guide presents dozens of professional techniques for writing OO code. In it, veteran developer Matthias Noback lays out design rules for constructing objects, defining methods, changing and exposing state, and much more ...
Hello World! 3rd Edition
Hello World! 3rd Edition

Hello World! Computer Programming for Kids and Other Beginners, 3rd Edition introduces the world of computer programming in a clear and fun style using Python, a programming language designed to be easy to learn. Learn to talk to your computer in its own language! Whether you want to create a game, start a business, or solve an important problem, the first step is learning to write your own programs. Hello World! Computer Programming for Kids and Other Beginners, 3rd Edition introduces the world of computer programming in a clear and fun style. Using Python, a programming language designed to be easy to learn, each engaging lesson teaches skills that apply to any kind of programming. It brings to life the basic concepts of computing - looping, decisions, input and output, graphics, and more. Written by father-and-son team Warren and Carter Sande, this international bestseller is kid-tested and reviewed by professional educators. Now in its third edition, Hello World! has been ...
Deep Learning with JavaScript
Deep Learning with JavaScript

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack. In Deep Learning with JavaScript, you ...
Pandas 1.x Cookbook, 2nd Edition
Pandas 1.x Cookbook, 2nd Edition

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. ...
Mastering Machine Learning Algorithms, 2nd Edition
Mastering Machine Learning Algorithms, 2nd Edition

Mastering Machine Learning Algorithms, 2nd Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy and Keras - to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensem ...
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