Book Image

Hands-On Machine Learning with Azure

By : Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak
Book Image

Hands-On Machine Learning with Azure

By: Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak

Overview of this book

Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
Table of Contents (14 chapters)

Building Deep Learning Solutions

Deep learning is a superset of machine learning incorporating algorithms influenced by the design and functionality of the human brain, known as the artificial intelligent neural network. It's represented in the form of supervised, semi-supervised, and unsupervised algorithms, where architectures profoundly concentrate on deep neural networks, deep belief networks, and recurrent neural networks. Deep learning today is widely accepted and utilized in industry as well as in R and D sectors in the field of computer vision, speech recognition, audio synthesis, image recognition, natural language processing, social media content moderation, and so on.

In this chapter, we will learn about the following topics:

  • An overview of Microsoft CNTK and the MMLSpark framework, along with third-party deep learning tools
  • TensorFlow and Keras, and the steps...