30 Days of ML
I’m taking the Stanford CS229 Machine Learning course to really understand how generative models like Stable Diffusion and DALL-E work.
I think my one of my strongest skills is synthesis - finding meaning from heaps of data and presenting it in a simple way, and I suspect that understanding how these engines work will not only be a useful career skill, but will teach me how to think better.
- Machine learning is how you teach computers to perform tasks without explicitly programming them.
- The most common form is “Supervised Learning” where you give the computer a dataset with inputs and labels (x,y). By observing the inputs and labels, the computer can figure out a mapping to predict y given x.
- On the other hand, in “Unsupervised Learning”, the computer is given a dataset with no labels (x) and asked to find something interesting in the dataset.
- There are different types of problems e.g. regression and classification.
- In a regression problem, the machine is trying to guess a continuous quantity e.g. given a dataset of house sizes to prices, predict the price of a house (given the size).
- In a classification problem, the machine is trying to guess a discrete outcome e.g. given a dataset of tumor sizes and malignancy, predict whether a tumor is malignant or not.
- In the Carnegie Mellon Experiment, a computer is taught to drive by taking screenshots of the the road 10x per second and matching it to a human’s actions on the steering wheel.
- The strategy you choose in ML is very important. This is known as Learning Theory.