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Podcasts
Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in.This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust.By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.
This book is an introduction to modeling and an in-depth look at topology in Blender, written by a Blender topology specialist with years of experience with the software. As you progress through its chapters, you’ll conquer the basics of quad-based topology using triangles and Ngons, and learn best practices and things to avoid while modeling and retopologizing. The pages are full of illustrations and examples with in-depth explanations that showcase each step in an easy-to-follow format.Squeaky Clean Topology in Blender starts by introducing you to the user interface and navigation. It then goes through an overview of the modeling techniques and hotkeys that will be necessary to understand the examples. With the modeling basics out of the way, the next stop on our journey is topology. Working through projects like a character and a sci-fi blaster, the book will illustrate and work through complex topology problems, and present solutions to those problems. These examples focus on deforming character models, non-deforming hard surface models, and optimizing these models by reducing the triangle count.By the end of this book, you will be able to identify the general flow of a shape's topology, identify and solve issues in your topology, and come out with a model ready for UV unwrapping, materials, and rigging.
Ionic Framework By Example. Click here to enter text
Change doesn’t have to be challenging. Sometimes it can be simple – sometimes it just makes sense. With Ionic, mobile development has never been so simple, so elegant and obvious. By helping developers to harness AngularJS and HTML5 for mobile development, it’s the perfect framework for anyone obsessed with performance, and anyone that understands just how important a great user experience really is. This book shows you how to get started with Ionic framework immediately. But it doesn’t just give you instructions and then expect you to follow them. Instead it demonstrates what Ionic is capable of through three practical projects you can follow and build yourself. From a basic to-do list app, a London tourist app, to a complete social media app, all three projects have been designed to help you learn Ionic at its very best. From setting up your project to developing on both the server side and front end, and best practices for testing and debugging your projects, you’ll quickly become a better mobile developer, delivering high performance mobile apps that look awesome. Ionic Framework by Example is for people who don’t want to learn now, build later – it’s for people who want to learn and build at the same time – so they can meet today’s mobile development challenges head on and deliver better products than anyone else.
AshishSingh Bhatia, Bostjan Kaluza
As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge.Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11.Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level.By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.
Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines
Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. All the sub-tasks are combined together once the required results are achieved; they are then merged to get the final output. The whole process is very complex. This process goes from the design of concurrent algorithms to the testing phase where concurrent applications need extra attention. Java includes a comprehensive API with a lot of ready-to-use components to implement powerful concurrency applications in an easy way, but with a high flexibility to adapt these components to your needs.The book starts with a full description of design principles of concurrent applications and how to parallelize a sequential algorithm. We'll show you how to use all the components of the Java Concurrency API from basics to the most advanced techniques to implement them in powerful concurrency applications in Java.You will be using real-world examples of complex algorithms related to machine learning, data mining, natural language processing, image processing in client / server environments. Next, you will learn how to use the most important components of the Java 8 Concurrency API: the Executor framework to execute multiple tasks in your applications, the phaser class to implement concurrent tasks divided into phases, and the Fork/Join framework to implement concurrent tasks that can be split into smaller problems (using the divide and conquer technique). Toward the end, we will cover the new inclusions in Java 8 API, the Map and Reduce model, and the Map and Collect model. The book will also teach you about the data structures and synchronization utilities to avoid data-race conditions and other critical problems. Finally, the book ends with a detailed description of the tools and techniques that you can use to test a Java concurrent application.