Shameful Blog

In this article, we provide a basic overview of loss iteration, gradient descent, and learning rates. Concisely: loss iteration is looping over different values to try and find increasingly more accurate weights for a model. Gradient descent is the idea that we can find more accurate models by examining the gradient of a given loss, and iterate over those gradients until we find one at the “bottom” of a convex shape. Learning rates are the amount of change in each of our “guesses” that try to get us to the most efficient values for a model

Derivatives allow us to determine the slope (or “rate of change”) at any given point in a graph. In this article, we examine how to understand and calculate basic derivatives

Loss is a quantification of how inaccurate a specific example’s prediction is from the actual data. For example: if a model predicts an example will have a value of 4.5, and it actually has a value of 4.2, the loss would be 0.3

Linear regression is about predicting data by determining a line that predicts future y values for a given x value

Here is a brief overview of some of the most common machine learning concepts: labels, features, examples, models, regression, and classification