Azure Infrastructure as Code
Azure Infrastructure as Code teaches you to use Azure's native infrastructure as code (IaC) tools, like ARM and Bicep, to build, manage, and scale infrastructure with just a few lines of code. You'll discover ARM templates, deployment stacks, and the powerful new language Bicep. See how easy they make it to create new environments, safely make infrastructure changes, govern your resources using Azure Policy, and prevent configuration drift. Loaded with in-depth coverage of syntax and lots of illustrative examples, this hands-on guide is a must-read for anyone looking to expand their knowledge of provisioning.
Automating tasks like provisioning servers, operating systems, and storage, saves time and radically increases consistency. The Infrastructure as Code (IaC) approach brings the tools and practices of application deployment, such as Github Actions, automated testing, and pipeline-driven deployments, to infrastructure components. With Azure's native IaC tools, you can create whol ...
Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose fun ...
Time Series Forecasting in Python
Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.
You can predict the future - with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before.
Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting ...
100 Go Mistakes and How to Avoid Them
100 Go Mistakes and How to Avoid Them puts a spotlight on common errors in Go code you might not even know you're making. You'll explore key areas of the language such as concurrency, testing, data structures, and more - and learn how to avoid and fix mistakes in your own projects. As you go, you'll navigate the tricky bits of handling JSON data and HTTP services, discover best practices for Go code organization, and learn how to use slices efficiently.
Understanding mistakes is the best way to improve the quality of your code. This unique book examines 100 bugs and inefficiencies common to Go applications, along with tips and techniques to avoid making them in your own projects.
100 Go Mistakes and How to Avoid Them shows you how to replace common programming problems in Go with idiomatic, expressive code. In it, you'll explore dozens of interesting examples and case studies as you learn to spot mistakes that might appear in your own applications. Expert author Teiva Harsanyi or ...
Secret Key Cryptography
Secret Key Cryptography gives you a toolbox of cryptographic techniques and Secret Key methods. The book's simple, non-technical language is easy to understand and accessible for any reader, even without the advanced mathematics normally required for cryptography. You'll learn how to create and solve ciphers, as well as how to measure their strength. As you go, you'll explore both historic ciphers and groundbreaking new approaches - including a never-before-seen way to implement the uncrackable One-Time Pad algorithm.
Whoever you are, this book is for you! History buffs will love seeing the evolution of sophisticated cryptographic methods, hobbyists will get a gentle introduction to cryptography, and engineers and computer scientists will learn the principles of constructing secure ciphers. Even professional cryptographers will find a range of new methods and concepts never published before.
From the Roman empire's Caesar cipher to the WWII Enigma machine, secret messages have in ...
Written by security engineer Dylan Shields, AWS Security provides comprehensive coverage on the key tools and concepts you can use to defend AWS-based systems. You'll learn how to honestly assess your existing security protocols, protect against the most common attacks on cloud applications, and apply best practices to configuring identity and access management and virtual private clouds.
AWS provides a suite of strong security services, but it's up to you to configure them correctly for your applications and data. Cloud platforms require you to learn new techniques for identity management, authentication, monitoring, and other key security practices. This book gives you everything you'll need to defend your AWS-based applications from the most common threats facing your business.
AWS Security is the guide to AWS security services you'll want on hand when you're facing any cloud security problem. Because it's organized around the most important security tasks, you'll quickly find ...
Effective Data Science Infrastructure
Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you'll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python.
The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.
Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from ...
Infrastructure as Code, Patterns and Practices
Infrastructure as Code, Patterns and Practices teaches you to automate infrastructure by applying changes in a codified manner. You'll learn how to create, test, and deploy infrastructure components in a way that's easy to scale and share across an entire organization. The book is full of flexible automation techniques that work whether you're managing your personal projects or making live network changes across a large enterprise.
A system administrator or infrastructure engineer will learn essential software development practices for managing IaC, while developers will benefit from in-depth coverage of assembling infrastructure as part of DevOps culture. While the patterns and techniques are tool agnostic, you'll appreciate the easy-to-follow examples in Python and Terraform.
Infrastructure as Code is a set of practices and processes for provisioning and maintaining infrastructure using scripts, configuration, or programming languages. With IaC in place, it's easy to test compo ...
Deep Learning with R, 2nd Edition
Deep Learning with R, 2nd Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.
Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.
Deep Learning with R, 2nd Edition is a hands-on guide to deep learning using the R language. As you move thr ...
Full Stack GraphQL Applications
The GraphQL query language radically reduces over-fetching or under-fetching of data by constructing precise graph-based data requests. In Full Stack GraphQL Applications you'll learn how to build graph-aware web applications that take full advantage of GraphQL's amazing efficiency. Neo4j's William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch. He reveals how you can build your web apps with GraphQL, React, Apollo, and Neo4j Database, aka "the GRANDstack," to get maximum performance out of GraphQL.
The GraphQL API query language radically streamlines data exchanges with backend servers by representing application data as easy-to-understand graphs. You can amplify GraphQL's benefits by using graph-aware tools and data stores, like React, Apollo, and Neo4j, throughout your application. A full stack graph approach provides a consistent data model end to end, reducing friction in data fetching and increasing developer productivit ...
R in Action, 3rd Edition
R in Action, 3rd Edition makes learning R quick and easy. That's why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you'll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R's state-of-the-art graphing capabilities with the ggplot2 package.
Used daily by data scientists, researchers, and quants of all types, R is the gold standard for statistical data analysis. This free and open source language includes packages for everything from advanced data visualization to deep learning. Instantly comfortable for mathematically minded users, R easily handles practical prob ...