Training Tips for Deep Learning (By Intel)
Full Course Link: https://www.coursera.org/learn/intro-... (An Introduction to Practical Deep Learning)
In this lecture we will provide some training tips and tricks that are maybe useful when training deep learning models. We will start with a quick review then discuss overfitting, data augmentation, and end with a discussion on training validation development and testing.
In classical machine learning, traditionally you have an image that is end by end, and you engineer a smaller set of K features. These features can be for example the ratio of the length to the height of the object, or the number of circle objects in the image.
Then you apply your valid algorithm to learn to associate these patterns of features with an identity, In this case, vehicle.
With supervised learning, we should learn algorithms used pre-labled training data to infer a function. Here imagine that you're trying to classify image into five categories. Vehicles, animals, faces, fruits, and chairs using two features, the two axises here. Supervised learning using the mission learning algorithms listed here allows for the determination of decision boundaries, shown here, using pre-labeled data. Here we can see that, while the decision boundaries are not perfect, they do a reasonable good job classifying the data. We'll now talk about overfitting, a common problem in machine learning.