Angular for Material Design
Build Angular applications faster and better with TypeScript and Material Design. You will learn how to build a web interface and use Google's open source Angular Material library of ready-made and easy-to-use components. This book uses Angular with TypeScript (a superset to JavaScript) to enable use of data types and take advantage of programming constructs such as classes, interfaces, generic templates, and more. You also will utilize various Angular features, including data binding, components, services, etc. You will build a single page application with the help of routing capabilities available out of the box (Angular CLI) and interface with remote services over HTTP. ...
Build Angular applications faster and better with TypeScript and Material Design. You will learn how to build a web interface and use Google's open source Angular Material library of ready-made and easy-to-use components. This book uses Angular with TypeScript (a superset to JavaScript) to enable use of data types and take advantage of programming constructs such as classes, interfaces, generic templates, and more. You also will utilize various Angular features, including data binding, components, services, etc. You will build a single page application with the help of routing capabilities available out of the box (Angular CLI) and interface with remote services over HTTP. ...
Operating Systems: From 0 to 1
This book helps you gain the foundational knowledge required to write an operating system from scratch. Hence the title, 0 to 1. After completing this book, at the very least you will learn: How to write an operating system from scratch by reading hardware datasheets. In the real world, it works like that. You won't be able to consult Google for a quick answer. A big picture of how each layer of a computer is related to the other, from hardware to software. Write code independently. It's pointless to copy and paste code. Real learning happens when you solve problems on your own. Some examples are given to kick start, but most problems are yours to conquer. However, the solutions are available online for you to examine after giving it a good try. Linux as a development environment and how to use common tools for low-level programming. x86 assembly in-depth. How a program is structured so that an operating system can run. How to debug a program running directly on hardware with gdb an ...
This book helps you gain the foundational knowledge required to write an operating system from scratch. Hence the title, 0 to 1. After completing this book, at the very least you will learn: How to write an operating system from scratch by reading hardware datasheets. In the real world, it works like that. You won't be able to consult Google for a quick answer. A big picture of how each layer of a computer is related to the other, from hardware to software. Write code independently. It's pointless to copy and paste code. Real learning happens when you solve problems on your own. Some examples are given to kick start, but most problems are yours to conquer. However, the solutions are available online for you to examine after giving it a good try. Linux as a development environment and how to use common tools for low-level programming. x86 assembly in-depth. How a program is structured so that an operating system can run. How to debug a program running directly on hardware with gdb an ...
NGINX Cookbook
NGINX is one of the most widely used web servers available today, in part because of its capabilities as a load balancer and reverse proxy server for HTTP and other network protocols.This cookbook provides easy-to-follow examples to real-world problems in application delivery. The practical recipes will help you set up and use either the open source or commercial offering to solve problems in various use cases. For professionals who understand modern web architectures, such as n-tier or microservice designs, and common web protocols including TCP and HTTP, these recipes provide proven solutions for security, software load balancing, and monitoring and maintaining NGINX's application delivery platform. You'll also explore advanced features of both NGINX and NGINX Plus, the free and licensed versions of this server. You'll find recipes for: High-performance load balancing with HTTP, TCP, and UDP; Securing access through encrypted traffic, secure links, HTTP authentication subreques ...
NGINX is one of the most widely used web servers available today, in part because of its capabilities as a load balancer and reverse proxy server for HTTP and other network protocols.This cookbook provides easy-to-follow examples to real-world problems in application delivery. The practical recipes will help you set up and use either the open source or commercial offering to solve problems in various use cases. For professionals who understand modern web architectures, such as n-tier or microservice designs, and common web protocols including TCP and HTTP, these recipes provide proven solutions for security, software load balancing, and monitoring and maintaining NGINX's application delivery platform. You'll also explore advanced features of both NGINX and NGINX Plus, the free and licensed versions of this server. You'll find recipes for: High-performance load balancing with HTTP, TCP, and UDP; Securing access through encrypted traffic, secure links, HTTP authentication subreques ...
Machine Learning Design Patterns
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models; Represent data for different ML model types, including embeddings, feature crosses, and more; Choose the right model type for specific problems; Build a robust training ...
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models; Represent data for different ML model types, including embeddings, feature crosses, and more; Choose the right model type for specific problems; Build a robust training ...
Deep Learning with Swift for TensorFlow
Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both with support for modern hardware accelerators and more. This book covers the deep learning concepts from fundamentals to advanced research. It also introduces the Swift language for beginners in programming. This book is well suited for newcomers and experts in programming and deep learning alike. After reading this book you should be able to program various state-of-the-art deep learning algorithms yourself. The book covers foundational concepts of machine learning. It also introduces the mathematics required to understand deep learning. Swift language is introduced such that it allows beginners and researchers to understand programming and easily transit to Swift for TensorFlow, respecti ...
Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both with support for modern hardware accelerators and more. This book covers the deep learning concepts from fundamentals to advanced research. It also introduces the Swift language for beginners in programming. This book is well suited for newcomers and experts in programming and deep learning alike. After reading this book you should be able to program various state-of-the-art deep learning algorithms yourself. The book covers foundational concepts of machine learning. It also introduces the mathematics required to understand deep learning. Swift language is introduced such that it allows beginners and researchers to understand programming and easily transit to Swift for TensorFlow, respecti ...
Kubeflow Operations Guide
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads-a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform; Understand the process of planning your Kubeflow deployment; Install Kubeflow on an existing on-premise Kubernetes cluster; Dep ...
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads-a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform; Understand the process of planning your Kubeflow deployment; Install Kubeflow on an existing on-premise Kubernetes cluster; Dep ...
The Node.js Handbook
Node.js is built on top of the Google Chrome V8 JavaScript engine, and it's mainly used to create web servers - but it's not limited to that. The Node.js Handbook follows the 80/20 rule: learn in 20% of the time the 80% of a topic. The author find this approach gives a well-rounded overview. ...
Node.js is built on top of the Google Chrome V8 JavaScript engine, and it's mainly used to create web servers - but it's not limited to that. The Node.js Handbook follows the 80/20 rule: learn in 20% of the time the 80% of a topic. The author find this approach gives a well-rounded overview. ...
TensorFlow 2.x in the Colaboratory Cloud
Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else - Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks - is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will fi ...
Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else - Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks - is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will fi ...
Learning Node.js
Node.js is an event-based, non-blocking, asynchronous I/O framework that uses Google's V8 JavaScript engine. It is used for developing applications that make heavy use of the ability to run JavaScript both on the client, as well as on server side and therefore benefit from the re-usability of code and the lack of context switching. It is open-source and cross-platform. Node.js applications are written in pure JavaScript and can be run within Node.js environment on Windows, Linux etc. It is an unofficial and free Node.js book created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow. ...
Node.js is an event-based, non-blocking, asynchronous I/O framework that uses Google's V8 JavaScript engine. It is used for developing applications that make heavy use of the ability to run JavaScript both on the client, as well as on server side and therefore benefit from the re-usability of code and the lack of context switching. It is open-source and cross-platform. Node.js applications are written in pure JavaScript and can be run within Node.js environment on Windows, Linux etc. It is an unofficial and free Node.js book created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow. ...
Learning TensorFlow.js
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learningand the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. - Explore tensors, the most fundamental structure of machine learning; - Convert data into tensors and back with a real-world example; - Combine AI with the web using TensorFlow.js; ...
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learningand the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. - Explore tensors, the most fundamental structure of machine learning; - Convert data into tensors and back with a real-world example; - Combine AI with the web using TensorFlow.js; ...