The power of deep learning cannot be understated. While Deep Learning (DL) is a subset of Machine Learning ML, which is itself a subset of Artificial Intelligence, a DL algorithm is able to learn hidden patterns from the data by itself, combine them together, and build much more efficient decision rules.
Deep learning works best as the amount of data scales, making it popular in industries that collect massive amounts of data. These industries include manufacturing, automotive, hospitality, healthcare, banking, agriculture, entertainment, IT/Security, retail, and supply chain and logistics.
There are different approaches to putting DL models into production with benefits that can vary dependent on the specific use case.
In this webinar, Praba Santhanakrishnan (a senior and experienced Microsoft executive who is long active in deep learning) will take you through both “the Big Picture” and the specific tools you can use to implement DL to solve various real world business problems. Praba will take you through some of the key nuances associated with:
One off training models
Batch training models
Real time training models
At the end of this webinar, you will be able to:
Understand the differences between Deep Learning, Machine Learning and Artificial Intelligence
Articulate the key ways in which Deep Learning is used today
Describe the differences in one off, batch and real time training models
Identify the most commonly used technologies used in Deep Learning
Understand some ways that Microsoft uses in Deep Learning tools in the cloud
Explain the principles of training and deploying models in production
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