From the course: Foundations of AI and Machine Learning for Java Developers
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Demo: Running VisRec JSR #381 - Java Tutorial
From the course: Foundations of AI and Machine Learning for Java Developers
Demo: Running VisRec JSR #381
- [Instructor] So as part of any AI ML application, you should focus on the data sets. The data engineering aspect of practically all AI production applications is crucial. Much of the time spent creating an AI production system is spent on data prep and data engineering. Without clean data, the AI application will not give you desired results. So for our example, we'll create a binary classifier. We're going to see whether we can create a simple model that can detect if an image is of a light colored chihuahua or not. So we need two data sets. One is of light colored chihuahuas and the other is of any image that's not a light colored chihuahua. And again, since there are two choices, we call this a binary classifier, which is very common. But however, application can have other types of classifiers that are not binary. But let's look at the binary classifier first. So in this example, I tell it, where am I training data? Where am I training data sets? And I have the training data…
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