Learning iBeacon

Craig Gilchrist

Hands-On Deep Learning with Go. A practical guide to building and implementing neural network models using Go

Gareth Seneque, Darrell Chua

Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.

Mastering Embedded Linux Development. Craft fast and reliable embedded solutions with Linux 6.6 and The Yocto Project 5.0 (Scarthgap) - Fourth Edition

Frank Vasquez, Chris Simmonds

Mastering Embedded Linux Development is designed to be both a learning resource and a reference for your embedded Linux projects.In this fourth edition, you'll learn the fundamental elements that underpin all embedded Linux projects: the toolchain, the bootloader, the kernel, and the root filesystem. First, you will download and install a pre-built toolchain. After that, you will cross-compile each of the remaining three elements from scratch and learn to automate the process using Buildroot and the Yocto Project. The book progresses with coverage of over-the-air software updates and rapid prototyping with add-on boards. Two new chapters tackle modern development practices, including Python packaging and deploying containerized applications. These are followed by a chapter on writing multithreaded code and another on techniques to manage memory efficiently. The final chapters demonstrate how to debug your code, whether it resides in user space or in the Linux kernel itself. In addition to GNU debugger (GDB), the book also covers the different tracers and profilers that are available for Linux so that you can quickly pinpoint any performance bottlenecks in your system.By the end of this book, you will be able to create efficient and secure embedded devices with Linux that will delight your users.

Dart: Scalable Application Development. Provides a solid foundation of libraries and tools

David Mitchell, Sergey Akopkokhyants, Ivo Balbaert

Designed to create next generation apps, Google’s Dart offers a much more robust framework and also supersedes JavaScript in several aspects. Familiar yet innovative, compact yet scalable, it blows away the accumulated JavaScript legacy limitations. Dart was designed for great tool-ability and developer productivity, allowing you to create better application faster than before. Google chose it for their billion dollar advertising business and you have its power for your projects too.The first module will introduce you the Dart language starting from its conception to its current form, and where it headed is through engaging substantial practical projects. You will be taken through building typical applications and exploring the exciting new technologies of HTML5.The second module will show you how to add internalization support to your web applications and how i18n and i10n access can be embedded into your code to design applications that can be localized easily. You will be shown how to organize client-to-server communication and how different HTML5 features can be used in Dart. Finally, this module will show you how you can store data locally, break the storage limit, and prevent security issues in your web application.The third module is a pragmatic guide that will increase your expertise in writing all kinds of applications, including web apps, scripts, and server-side apps. It provides rich insights on how to extend your Dart programming skills. Altogether, this course provides you the power to create powerful applications with Dart, without worrying about your knowledge leading to you having to make compromises to the end product!This Learning Path has been curated from three Packt products:Dart By Example By Davy MitchellMastering Dart By Sergey AkopkokhyantsDart Cookbook By Ivo Balbaert

Learn Kubernetes Security. Securely orchestrate, scale, and manage your microservices in Kubernetes deployments

Kaizhe Huang, Pranjal Jumde, Loris Degioanni

Kubernetes is an open source orchestration platform for managing containerized applications. Despite widespread adoption of the technology, DevOps engineers might be unaware of the pitfalls of containerized environments. With this comprehensive book, you'll learn how to use the different security integrations available on the Kubernetes platform to safeguard your deployments in a variety of scenarios.Learn Kubernetes Security starts by taking you through the Kubernetes architecture and the networking model. You'll then learn about the Kubernetes threat model and get to grips with securing clusters. Throughout the book, you'll cover various security aspects such as authentication, authorization, image scanning, and resource monitoring. As you advance, you'll learn about securing cluster components (the kube-apiserver, CoreDNS, and kubelet) and pods (hardening image, security context, and PodSecurityPolicy). With the help of hands-on examples, you'll also learn how to use open source tools such as Anchore, Prometheus, OPA, and Falco to protect your deployments.By the end of this Kubernetes book, you'll have gained a solid understanding of container security and be able to protect your clusters from cyberattacks and mitigate cybersecurity threats.

Deep Learning with TensorFlow. Explore neural networks and build intelligent systems with Python - Second Edition

Giancarlo Zaccone, Md. Rezaul Karim

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models

Vincent Vandenbussche, Akin Osman Kazakci

Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you’ll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You’ll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you’ll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you’ll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you’ll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E.By the end of this book, you’ll be armed with different regularization techniques to apply to your ML and DL models.

Cloud Observability with Azure Monitor. A practical guide to monitoring your Azure infrastructure and applications using industry best practices

José Ángel Fernández, Manuel Lázaro Ramírez

Cloud observability is complex and costly due to the use of hybrid and multi-cloud infrastructure as well as various Azure tools, hampering IT teams’ ability to monitor and analyze issues. The authors distill their years of experience with Microsoft to share the strategic insights and practical skills needed to optimize performance, ensure reliability, and navigate the dynamic landscape of observability on Azure.You’ll get an in-depth understanding of cloud observability and Azure Monitor basics, before getting to grips with the configuration and optimization of data sources and pipelines for effective monitoring. You’ll learn about advanced data analysis techniques using metrics and the Kusto Query Language (KQL) for your logs, design proactive incident response strategies with automated alerts, and visualize reports via dashboards. Using hands-on examples and best practices, you’ll explore the integration of Azure Monitor with Azure Arc and third-party tools, such as Datadog, Elastic Stack, or Dynatrace. You’ll also implement artificial intelligence for IT Operations (AIOps) and secure monitoring for hybrid and multi-cloud environments, aligned with emerging trends.By the end of this book, you’ll be able to develop robust and cost-optimized observability solutions for monitoring your Azure infrastructure and apps using Azure Monitor.