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The Basics of Machine Llearning, Deep Learning and TensorFlow*

Hi. I'm Meghana Rao. And this is the AI from the Data Center to the Edge video series. In this episode, you will learn how to analyze a trained deep learning module. We also assess how well the model is doing against preset thresholds.

The goal is to iteratively test the model and tune hyperparameters until we get a well-trained model. Alternatively, this step also tells us if we need to switch to a different network to achieve expected results.

Let's take a closer look at what's involved in [the] model analysis. The first step is [an] assessment of the model based on metrics, such as accuracy, training and inference speeds, and the size of the model. If the model is not performing well against [the] preset thresholds for the above metrics, we tune [the] hyperparameters, such as gradient descent, momentum, solver type, and batch size.

This, as we learn in the course, is an iterative process. We repeat training with the same network until [the] desired results are obtained, or we choose a different network and repeat. The course teaches techniques like confusion matrix, classification report, and ROC plot for model analysis.

Thanks for watching this episode of AI from the Data Center to the Edge. Make sure to check out the links to register for the course. You can complete the lecture and the notebooks listed in the resources for this course, and join me in the next episode to learn more about deploying a model to the edge.