Your simple guide to gradient descent
Deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, are diverse, each designed for specific tasks. Despite their diversity, they share a common principle—gradient descent, a technique for training these models.
The mathematics behind deep learning can be complex, but Ronald T. Kneusel's book, Math for Deep Learning, makes it accessible through examples, Python code, and visuals.
In my latest article on TechTalks, I simplify the concept of gradient descent, without delving too deeply into the mathematical intricacies.
Key points:
The goal of gradient descent is to adjust the parameters of a model in a way that finds the minimum value for the loss function
Gradient descent iteratively adjusts the parameters of a model by calculating the slope of the loss function and moving in the opposite direction
In single-parameter models, we use simple derivatives to calculate the gradient
In multi-parameter models, we use partial derivatives to calculate gradients
In deep neural networks, we use backpropagation and the chain rule to calculate gradients
Most real-world problems don’t have multiple minima—fortunately, gradient descent helps find one of the many suitable minima
When you use the entire training dataset for each step of training, we call it “batch gradient descent”
When you use a subset of the examples, we call it “stochastic gradient descent”
Read the full article on TechTalks
Some goodies:
Regardless of where you are in your deep learning journey, I highly recommend Math for Deep Learning—I learned a lot from it
For a more applied book on machine learning and deep learning, I recommend Aurelien Geron’s Hands-on Machine Learning 3rd Edition
For more AI explainers:
Hey Ben, great article on gradient descent. It's impressive how you simplified the concept without losing its essence. Looking forward to more of your insightful explainers on TechTalks. Keep up the fantastic work.