From the course: Amazon Web Services Machine Learning Essential Training
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Understanding deep learning - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services Machine Learning Essential Training
Understanding deep learning
- [Instructor] So when I'm working with my clients in trying to figure out whether or not using server clusters is the right approach for working with machine learning, there are a couple of considerations. One is the size and complexity of the input data. And the second is the complexity of the algorithm or algorithms that are going to be used. Very commonly, if customers are interested in trying out deep learning, we'll use server clusters. So what is deep learning? And how is it different from traditional machine learning? If we consider the use case of trying to build a model that labels which animal is in a photo, the cat or dog, sort of classic use case, in traditional machine learning, you'll use a classifier algorithm. But that classifier needs a whole bunch of trained or labeled data. And that has to be done by people, and that's called feature extraction. This process of creating this trained or labeled data can really slow down the building and implementation of the model…
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Contents
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Understanding ML virtual servers4m 7s
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Understanding deep learning2m 36s
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Work with Gluon for MXNet in SageMaker5m 14s
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Work with MXNet in SageMaker9m 1s
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Databricks on AWS7m 2s
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Work with MXNet in Databricks9m 2s
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Set up the AWS Deep Learning AMIs6m 38s
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Work with the AWS Deep Learning AMI4m 16s
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Work with EMR for machine learning8m 40s
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