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)

What is deep learning?

Deep learning is a subclass of traditional machine learning algorithms that utilizes a series of non-linear processing layers for feature extraction, transformation, and, finally, analysis over the successive layers of output from the previous layers of input.

The first layer of the deep learning neural network consists of an input layer, an output layer (the outermost layer), and a hidden layer, which is a complex layer in-between the input and output layers:

Differences between traditional machine learning and deep learning

The comparison between traditional machine learning and deep learning are as follows:

Traditional Machine learning Deep learning
Traditional machine learning needs manual...