An expansive guide for resistance and solidarity across this storied region.   Richmond and Central Virginia are a historic epicenter of America’s racialized history. This alternative guidebook foregrounds diverse communities in the region who are mobilizing to dismantle oppressive systems and fundamentally transforming the space to live and thrive. Featuring personal reflections from activists, artists, and community leaders, this book eschews colonial monuments and confederate memorials to instead highlight movements, neighborhoods, landmarks, and gathering spaces that shape social justice struggles across the history of this rapidly growing area.   The sites, stories, and events featured here reveal how community resistance and resilience remain firmly embedded in the region’s landscape. A People’s Guide to Richmond and Central Virginia counters the narrative that elites make history worth knowing, and sites worth visiting, by demonstrating how ordinary people come together to create more equitable futures.

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key Features Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure Improve the accuracy of your ML models through automatic data featurization and model training Increase productivity in your organization by using artificial intelligence to solve common problems Book DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learn Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML Prepare data for Azure AutoML to ensure smooth model training and deployment Adjust AutoML configuration settings to make your models as accurate as possible Determine when to use a batch-scoring solution versus a real-time scoring solution Productionalize your AutoML and discover how to quickly deliver value Create real-time scoring solutions with AutoML and Azure Kubernetes Service Train a large number of AutoML models at once using the AzureML Python SDK Who this book is forData scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.