Biznes IT
Mastering Arduino. A project-based approach to electronics, circuits, and programming
Jon Hoffman
Mastering Arduino is an all-in-one guide to getting the most out of your Arduino. This practical, no-nonsense guide teaches you all of the electronics and programming skills that you need to create advanced Arduino projects. This book is packed full of real-world projects for you to practice on, bringing all of the knowledge in the book together and giving you the skills to build your own robot from the examples in this book. The final two chapters discuss wireless technologies and how they can be used in your projects. The book begins with the basics of electronics, making sure that you understand components, circuits, and prototyping before moving on. It then performs the same function for code, getting you into the Arduino IDE and showing you how to connect the Arduino to a computer and run simple projects on your Arduino.Once the basics are out of the way, the next 10 chapters of the book focus on small projects centered around particular components, such as LCD displays, stepper motors, or voice synthesizers. Each of these chapters will get you familiar with the technology involved, how to build with it, how to program it, and how it can be used in your own projects.
Christoph Körner, Marcel Alsdorf
Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps.The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning.The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets.By the end of this book, you’ll be able to combine all the steps you’ve learned by building an MLOps pipeline.
Christoph Körner, Kaijisse Waaijer
The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud.The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline.By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.
Krishnendu Kar
Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
Merunas Grincalaitis
Ethereum is one of the commonly used platforms for building blockchain applications. It's a decentralized platform for applications that can run exactly as programmed without being affected by fraud, censorship, or third-party interference.This book will give you a deep understanding of how blockchain works so that you can discover the entire ecosystem, core components, and its implementations. You will get started by understanding how to configure and work with various Ethereum protocols for developing dApps. Next, you will learn to code and create powerful smart contracts that scale with Solidity and Vyper. You will then explore the building blocks of the dApps architecture, and gain insights on how to create your own dApp through a variety of real-world examples. The book will even guide you on how to deploy your dApps on multiple Ethereum instances with the required best practices and techniques. The next few chapters will delve into advanced topics such as, building advanced smart contracts and multi-page frontends using Ethereum blockchain. You will also focus on implementing machine learning techniques to build decentralized autonomous applications, in addition to covering several use cases across a variety of domains such as, social media and e-commerce. By the end of this book, you will have the expertise you need to build decentralized autonomous applications confidently.
Silas Toms, Paul Crickard, Eric van Rees
Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis.You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
Mastering Hadoop 3. Big data processing at scale to unlock unique business insights
Chanchal Singh, Manish Kumar
Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency.With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals.By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Mastering IOT. Build modern IoT solutions that secure and monitor your IoT infrastructure
Colin Dow, Perry Lea
The Internet of Things (IoT) is the fastest growing technology market. Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being.We’ll begin our journey with an introduction to Raspberry Pi and quickly jump right into Python programming. We’ll learn all concepts through multiple projects, and then reinforce our learnings by creating an IoT robot car. We’ll examine modern sensor systems and focus on what their power and functionality can bring to our system. We’ll also gain insight into cloud and fog architectures, including the OpenFog standards. The Learning Path will conclude by discussing three forms of prevalent attacks and ways to improve the security of our IoT infrastructure.By the end of this Learning Path, we will have traversed the entire spectrum of technologies needed to build a successful IoT system, and will have the confidence to build, secure, and monitor our IoT infrastructure.This Learning Path includes content from the following Packt products:Internet of Things Programming Projects by Colin DowInternet of Things for Architects by Perry Lea