Deep learning is what happens when neural networks get many layers. Stacking layers lets a model build up complex features from simple ones, which is why it powers so much of modern AI. This module builds on the basics of weights, gradient descent, and backpropagation and shows how deep networks are shaped for different kinds of data.
The first lesson is a full university-style introduction to deep learning foundations. The next two are short, visual explanations of the two classic architectures: convolutional neural networks (CNNs), built for images, and recurrent neural networks (RNNs), built for sequences like text and time series.
Watch the lessons in order. If a video runs long, feel free to treat it as a reference you dip back into later rather than something to finish in one sitting.
Alexander Amini's opening lecture from MIT's deep learning course: why deep learning took off, how perceptrons stack into deep networks, and how they are trained and kept from overfitting.
Video 1h 9minStatQuest shows how CNNs use filters and pooling to recognize images, and why they handle pictures far better than a plain fully connected network.
Video 15minHow RNNs use feedback loops to handle sequences of varying length, such as sentences or stock prices, and why they struggle with vanishing and exploding gradients.
Video 16min