Deep Learning Basics: A Crash Course

That is, the network has learned the generic shape of a feature, such as a mouth or a nose, and can detect this feature in the input data despite variations it might have. In the second row of the preceding image, we can see how the deeper layers of the network combine these features into even more complex ones, such as faces and whole cars. A strength of deep neural networks is that they can learn these high-level abstract representations themselves by deducing them from the training data. We could define deep learning as a class of machine learning techniques where information is processed in hierarchical layers to understand representations and features from data in increasing levels of complexity. In practice, all deep learning algorithms are neural networks, which share some common basic properties.