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An expansive guide for resistance and solidarity across this
storied region. Â 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. Â 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.
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