Mastering .NET Machine Learning. Use machine learning in your .NET applications

Jamie Dixon

.Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines.This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions.You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results.Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly.

Protocol Buffers Handbook. Getting deeper into Protobuf internals and its usage

Clément Jean

Explore how Protocol Buffers (Protobuf) serialize structured data and provides a language-neutral, platform-neutral, and extensible solution. With this guide to mastering Protobuf, you'll build your skills to effectively serialize, transmit, and manage data across diverse platforms and languages.This book will help you enter the world of Protocol Buffers by unraveling the intricate nuances of Protobuf syntax and showing you how to define complex data structures. As you progress, you’ll learn schema evolution, ensuring seamless compatibility as your projects evolve. The book also covers advanced topics such as custom options and plugins, allowing you to tailor validation processes to your specific requirements. You’ll understand how to automate project builds using cutting-edge tools such as Buf and Bazel, streamlining your development workflow. With hands-on projects in Go and Python programming, you’ll learn how to practically apply Protobuf concepts. Later chapters will show you how to integrate data interchange capabilities across different programming languages, enabling efficient collaboration and system interoperability.By the end of this book, you’ll have a solid understanding of Protobuf internals, enabling you to discern when and how to use and redefine your approach to data serialization.

Learning Unreal Engine Game Development. A step-by-step guide that paves the way for developing fantastic games with Unreal Engine 4

Joanna Lee

Unreal Engine 4 is a powerful game development engine that provides rich functionalities to create 2D and 3D games across multiple platforms. Many people know what a game is and they play games every day, but how many of them know how to create a game? Unreal Engine technology powers hundreds of games, and thousands of individuals have built careers and companies around skills developed using this engine.Learning Unreal Engine 4 Game Development starts with small, simple game ideas and playable projects that you can actually finish. The book first teaches you the basics of using Unreal Engine to create a simple game level. Then, you'll learn how to add details such as actors, animation, effects, and so on to the game. The complexity will increase over the chapters and the examples chosen will help you learn a wide variety of game development techniques. This book aims to equip you with the confidence and skills to design and build your own games using Unreal Engine 4. By the end of this book, you'll have learnt about the entire Unreal suite and know how to successfully create fun, simple games.

Instant Creating Data Models with PowerPivot How-to. Build better business intelligence with this practical guide to creating Excel data models with PowerPivot

Leo Taehyung Lee, Taehyung Lee

Microsoft PowerPivot is a free software designed to enhance Microsoft Excel. It allows the user to make extensive use of his/her computer's powers to draw data from various sources, do analysis across millions of rows of data, and present results in a very unique format.Instant Creating Data Models with PowerPivot How-to is a concise and to-the-point guide that helps you get a jump start on using this strong business intelligence tool, while still working in an environment similar to Excel. You will begin with data generation and manipulation, learning a new feature at each step by building onto the old file, and ultimately creating a comprehensive file.Instant Creating Data Models with PowerPivot How-to will guide the user through database installation, importing data from various sources, creating pivot charts and tables, utilizing a unique feature of PowerPivot called slicers,adding custom columns, and setting custom relationships between data to generate the ultimate customized dataset for analysis. By the end of the book and all the sections of Microsoft PowerPivot for Excel, the reader will be fully experienced and ready to utilize this powerful software.

Power Up Your PowToon Studio Project. The ultimate PowToon project guide – get hints, tips, and ideas to turbocharge your PowToon Studio project

Bruce Graham

Backbone.js Patterns and Best Practices. Improve your Backbone.js skills with this step-by-step guide to patterns and best practice. It will help you reduce boilerplate in your code and provide plenty of open source plugin solutions to common problems along the way

Swarnendu De

The Art of Micro Frontends. Build websites using compositional UIs that grow naturally as your application scales

Florian Rappl, Lothar Schöttner

Micro frontend is a web architecture for frontend development borrowed from the idea of microservices in software development, where each module of the frontend is developed and shipped in isolation to avoid complexity and a single point of failure for your frontend.Complete with hands-on tutorials, projects, and self-assessment questions, this easy-to-follow guide will take you through the patterns available for implementing a micro frontend solution. You’ll learn about micro frontends in general, the different architecture styles and their areas of use, how to prepare teams for the change to micro frontends, as well as how to adjust the UI design for scalability. Starting with the simplest variants of micro frontend architectures, the book progresses from static approaches to fully dynamic solutions that allow maximum scalability with faster release cycles. In the concluding chapters, you'll reinforce the knowledge you’ve gained by working on different case studies relating to micro frontends.By the end of this book, you'll be able to decide if and how micro frontends should be implemented to achieve scalability for your user interface (UI).

Practical Machine Learning on Databricks. Seamlessly transition ML models and MLOps on Databricks

Debu Sinha

Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.