Lesson 13: Deep Learning Foundations to Stable Diffusion
(All lesson resources are available at http://course.fast.ai.) In this lesson, we dive into backpropagation and the creation of a simple Multi-Layer Perceptron (MLP) neural network. We start by reviewing basic neural networks and their architecture, then move on to implementing a simple MLP from scratch. We focus on understanding the chain rule and backpropagation in the context of neural networks, and demonstrate how to calculate derivatives using Python and the SimPy library.
We also discuss the importance of the chain rule in calculating the gradient of the mean squared error (MSE) applied to a model, and demonstrate how to use PyTorch to calculate derivatives and simplify the process by creating classes for ReLU and linear functions. We then explore the issues with floating point math and introduce the log sum exp trick to overcome these issues. Finally, we create a training loop for a simple neural network.
0:00 - Introduction
2:54 - Linear models & rectified lines (ReLU) diagram
10:15 - Multi Layer Perceptron (MLP) from scratch
18:15 - Loss function from scratch - Mean Squared Error (MSE)
23:14 - Gradients and backpropagation diagram
31:30 - Matrix calculus resources
33:27 - Gradients and backpropagation code
38:15 - Chain rule visualized + how it applies
49:08 - Using Python’s built in debugger
1:00:47 - Refactoring the code
Thank you Raymond-Wu on forums.fast.ai for creating the timestamps, and to fmussari for the transcript.
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