Learning ZeroMQ
Even connecting a few programs across a few sockets is plain nasty when you start to handle real life situations. Trillions? The cost would be unimaginable. Connecting computers is so difficult that software and services to do this is a multi-billion dollar business. So today we're still connecting applications using raw UDP and TCP, proprietary protocols, HTTP, Websockets. It remains painful, slow, hard to scale, and essentially centralized. To fix the world, we needed to do two things. One, to solve the general problem of "how to connect any code to any code, anywhere". Two, to wrap that up in the simplest possible building blocks that people could understand and use easily. It sounds ridiculously simple. And maybe it is. That's kind of the whole point. If you are a programmer and you aim to build large systems, in any language, then Code Connected is essential reading. Code Connected Volume 1 takes you through learning ZeroMQ, step-by-step, with over 80 examples. You will learn t ...
Even connecting a few programs across a few sockets is plain nasty when you start to handle real life situations. Trillions? The cost would be unimaginable. Connecting computers is so difficult that software and services to do this is a multi-billion dollar business. So today we're still connecting applications using raw UDP and TCP, proprietary protocols, HTTP, Websockets. It remains painful, slow, hard to scale, and essentially centralized. To fix the world, we needed to do two things. One, to solve the general problem of "how to connect any code to any code, anywhere". Two, to wrap that up in the simplest possible building blocks that people could understand and use easily. It sounds ridiculously simple. And maybe it is. That's kind of the whole point. If you are a programmer and you aim to build large systems, in any language, then Code Connected is essential reading. Code Connected Volume 1 takes you through learning ZeroMQ, step-by-step, with over 80 examples. You will learn t ...
AI for Healthcare with Keras and Tensorflow 2.0
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you c ...
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you c ...
How To Code in React.js
This open book is an introduction to React that works from the foundations upward. Each chapter takes you a little deeper into the React ecosystem, building on your previous knowledge. Along the way, you'll learn how to maintain internal state, pass information between parts of an application, and explore different options for styling your application. Whether you are completely new to React or if you've worked with it before, this series will be accessible to you. Every chapter is self contained, so you can jump between chapters or skip whole sections. The book is designed for you to take a concept and explore it by building a small project that mirrors what you will encounter in everyday development using React. ...
This open book is an introduction to React that works from the foundations upward. Each chapter takes you a little deeper into the React ecosystem, building on your previous knowledge. Along the way, you'll learn how to maintain internal state, pass information between parts of an application, and explore different options for styling your application. Whether you are completely new to React or if you've worked with it before, this series will be accessible to you. Every chapter is self contained, so you can jump between chapters or skip whole sections. The book is designed for you to take a concept and explore it by building a small project that mirrors what you will encounter in everyday development using React. ...
AI and Machine Learning for On-Device Development
AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today. Explore the options for implementing ML and AI on mobile devices; Create ML models for iOS and Android; Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS; Choose the best techniques and tools for your use case, such as cloud-based versus on-device infere ...
AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today. Explore the options for implementing ML and AI on mobile devices; Create ML models for iOS and Android; Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS; Choose the best techniques and tools for your use case, such as cloud-based versus on-device infere ...
Transfer Learning for Natural Language Processing
Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You'll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you'll save on training time and computational costs. Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions ...
Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You'll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you'll save on training time and computational costs. Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions ...
Deep Learning Patterns and Practices
The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch's work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You'll build your skills and confidence with each interesting example. Deep Learning Patterns and Practices is a deep dive into building successful deep ...
The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch's work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You'll build your skills and confidence with each interesting example. Deep Learning Patterns and Practices is a deep dive into building successful deep ...
Learning Test-Driven Development
Your code is a testament to your skills as a developer. No matter what language you use, code should be clean, elegant, and uncluttered. By using test-driven development (TDD), you'll write code that's easy to understand, retains its elegance, and works for months, even years, to come. With this indispensable guide, you'll learn how to use TDD with three different languages: Go, JavaScript, and Python. Author Saleem Siddiqui shows you how to tackle domain complexity using a unit test-driven approach. TDD partitions requirements into small, implementable features, enabling you to solve problems irrespective of the languages and frameworks you use. With Learning Test-Driven Development at your side, you'll learn how to incorporate TDD into your regular coding practice. This book helps you: Use TDD's divide-and-conquer approach to tame domain complexity; Understand how TDD works across languages, testing frameworks, and domain concepts; Learn how TDD enables continuous integration; ...
Your code is a testament to your skills as a developer. No matter what language you use, code should be clean, elegant, and uncluttered. By using test-driven development (TDD), you'll write code that's easy to understand, retains its elegance, and works for months, even years, to come. With this indispensable guide, you'll learn how to use TDD with three different languages: Go, JavaScript, and Python. Author Saleem Siddiqui shows you how to tackle domain complexity using a unit test-driven approach. TDD partitions requirements into small, implementable features, enabling you to solve problems irrespective of the languages and frameworks you use. With Learning Test-Driven Development at your side, you'll learn how to incorporate TDD into your regular coding practice. This book helps you: Use TDD's divide-and-conquer approach to tame domain complexity; Understand how TDD works across languages, testing frameworks, and domain concepts; Learn how TDD enables continuous integration; ...
MERN Projects for Beginners
Learn how to use the MERN stack (MongoDB, Express.js, React, and Node) to build five fully functioning web apps for dating, video sharing, messaging, and social media. While creating these web apps, you'll learn key development concepts including how to use React hooks, Redux, MongoDB, Express, Heroku, Firebase, Material UI, and Google authentication. By expanding your portfolio with the projects you create, you will be well equipped as front-end developer. You will first create a dating site with a swiping feature and chat functionality. You will then build a video sharing app with videos displaying vertically. Next, you will learn to build an awesome messaging web app. Users will be able to chat in real time, as well as log in to their account using Google authentication. You will also create a photo sharing app and social media web apps with the ability to post images with captions and log in using email and password authentication. Most MERN tutorials out there today cover ba ...
Learn how to use the MERN stack (MongoDB, Express.js, React, and Node) to build five fully functioning web apps for dating, video sharing, messaging, and social media. While creating these web apps, you'll learn key development concepts including how to use React hooks, Redux, MongoDB, Express, Heroku, Firebase, Material UI, and Google authentication. By expanding your portfolio with the projects you create, you will be well equipped as front-end developer. You will first create a dating site with a swiping feature and chat functionality. You will then build a video sharing app with videos displaying vertically. Next, you will learn to build an awesome messaging web app. Users will be able to chat in real time, as well as log in to their account using Google authentication. You will also create a photo sharing app and social media web apps with the ability to post images with captions and log in using email and password authentication. Most MERN tutorials out there today cover ba ...
Graph-Powered Machine Learning
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural ...
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural ...
Deep Learning with Python, 2nd Edition
Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach - even if you have no background in mathematics or data science. This book shows you how to get started. Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras cre ...
Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach - even if you have no background in mathematics or data science. This book shows you how to get started. Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras cre ...