Table of contents
About this book
- 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
Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft’s Cloud AI platform.
Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.
- DOI https://doi.org/10.1007/978-1-4842-3679-6
- Copyright Information Mathew Salvaris, Danielle Dean, Wee Hyong Tok 2018
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Print ISBN 978-1-4842-3678-9
- Online ISBN 978-1-4842-3679-6
- Buy this book on publisher's site