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Get up-to-speed with Microsoft's AI Platform. Learn to innovate and
accelerate with open and powerful tools and services that bring
artificial intelligence to every data scientist and developer.
Artificial Intelligence (AI) is the new normal. Innovations in deep
learning algorithms and hardware are happening at a rapid pace. It
is no longer a question of should I build AI into my business, but
more about where do I begin and how do I get started with AI?
Written by expert data scientists at Microsoft, Deep Learning with
the Microsoft AI Platform helps you with the how-to of doing deep
learning on Azure and leveraging deep learning to create innovative
and intelligent solutions. Benefit from guidance on where to begin
your AI adventure, and learn how the cloud provides you with all
the tools, infrastructure, and services you need to do AI. What
You'll Learn Become familiar with the tools, infrastructure, and
services available for deep learning on Microsoft Azure such as
Azure Machine Learning services and Batch AI Use pre-built AI
capabilities (Computer Vision, OCR, gender, emotion, landmark
detection, and more) Understand the common deep learning models,
including convolutional neural networks (CNNs), recurrent neural
networks (RNNs), generative adversarial networks (GANs) with sample
code and understand how the field is evolving Discover the options
for training and operationalizing deep learning models on Azure Who
This Book Is For Professional data scientists who are interested in
learning more about deep learning and how to use the Microsoft AI
platform. Some experience with Python is helpful.
Predictive Analytics with Microsoft Azure Machine Learning, Second
Edition is a practical tutorial introduction to the field of data
science and machine learning, with a focus on building and
deploying predictive models. The book provides a thorough overview
of the Microsoft Azure Machine Learning service released for
general availability on February 18th, 2015 with practical guidance
for building recommenders, propensity models, and churn and
predictive maintenance models. The authors use task oriented
descriptions and concrete end-to-end examples to ensure that the
reader can immediately begin using this new service. The book
describes all aspects of the service from data ingress to applying
machine learning, evaluating the models, and deploying them as web
services. Learn how you can quickly build and deploy sophisticated
predictive models with the new Azure Machine Learning from
Microsoft. What's New in the Second Edition? Five new chapters have
been added with practical detailed coverage of: Python Integration
- a new feature announced February 2015 Data preparation and
feature selection Data visualization with Power BI Recommendation
engines Selling your models on Azure Marketplace
Most data scientists and engineers today rely on quality labeled
data to train machine learning models. But building a training set
manually is time-consuming and expensive, leaving many companies
with unfinished ML projects. There's a more practical approach. In
this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you
how to create products using weakly supervised learning models.
You'll learn how to build natural language processing and computer
vision projects using weakly labeled datasets from Snorkel, a
spin-off from the Stanford AI Lab. Because so many companies have
pursued ML projects that never go beyond their labs, this book also
provides a guide on how to ship the deep learning models you build.
Get up to speed on the field of weak supervision, including ways to
use it as part of the data science process Use Snorkel AI for weak
supervision and data programming Get code examples for using
Snorkel to label text and image datasets Use a weakly labeled
dataset for text and image classification Learn practical
considerations for using Snorkel with large datasets and using
Spark clusters to scale labeling
Develop smart applications without spending days and weeks building
machine-learning models. With this practical book, you'll learn how
to apply Automated Machine Learning, a process that uses machine
learning to help people build machine learning models. Deepak
Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of
technical depth, hands-on examples, and case studies that show how
customers are solving real-world problems with this technology.
Building machine learning models is an iterative and time-consuming
process. Even those who know how to create these models may be
limited in how much they can explore. Once you complete this book,
you'll understand how to apply Automated Machine Learning to your
data right away. Learn how companies in different industries are
benefiting from Automated Machine Learning Get started with
Automated Machine Learning using Azure Explore aspects such as
algorithm selection, auto featurization, and hyperparameter tuning
Understand how data analysts, BI professionals, and developers can
use Automated Machine Learning in their familiar tools and
experiences Learn how to get started using Automated Machine
Learning for use cases including classification and regression.
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