Courage to Learn ML: Explain Backpropagation from Mathematical Theory to Coding Practice
Welcome back to the latest chapter of ‘Courage to Learn ML. In this series, I aim to demystify complex ML topics and make them engaging through a Q&A format.
This time, our learner is exploring backpropagation and has chosen to approach it through coding. He discovered a Python tutorial on Machine Learning Mastery, which explains backpropagation from scratch using basic Python, without any deep learning frameworks. Finding the code a bit puzzling, he visited the mentor and asked for guidance to better understand both the code and the concept of backpropagation.
As always, here's a list of the topics we'll be exploring today:
- Understanding backpropagation and its connection to gradient Descent
- Exploring the preference for depth over width in DNNs and the rarity of shallow, wide networks.
- What is the chain rule?
- Breaking down backpropagation calculation into 3 components and examining each thoroughly. Why is it called backpropagation?
- Understand backpropagation through straightforward Python code
- Gradient vanishing and common preference in activation functions
Let's start with the fundamental why –
What is backpropagation and how is it related to gradient descent?
Gradient descent is a key optimization method in Machine Learning. It's not just limited to training DNNs but is also used to train models like logistic and linear regression. The fundamental idea behind it is that by minimizing the differences between predictions and true labels (prediction error), our model will get closer to the underlying true model. In gradient descent, the gradient, represented by ∇
