Machine Learning Intuition: Understanding Taylor Series Approximation

We have talked before about the intuition behind cost function optimization in machine learning. We took a look at where cost functions come from and what they look like. We also talked about how one might get to the bottom of one with gradient descent.

When you open a machine learning textbook, you’ll see much more math than we used in that introduction. But the math is, in fact, important; the math gives us tools that we can use to quickly find the minima of our cost functions.

We talked about derivatives in the last post, and we’re talking about Taylor series in this post. Both of these tools come from calculus and help us identify where to find the minima on our cost functions.

Continue reading “Machine Learning Intuition: Understanding Taylor Series Approximation”

Machine Learning Intuition: Using Derivatives to Minimize the Cost Function

We have talked before about the intuition behind cost function optimization in machine learning. We took a look at where cost functions come from and what they look like. We also talked about how one might get to the bottom of one with gradient descent.

When you open a machine learning textbook, you’ll see much more math than we used in that introduction. But the math is, in fact, important; the math gives us tools that we can use to quickly find the minima of our cost functions.

We’re going to talk about two of those tools: derivatives (in this post) and Taylor series (in the next post). Both of these tools come from calculus and help us identify where to find the minima on our cost functions.

Continue reading “Machine Learning Intuition: Using Derivatives to Minimize the Cost Function”

Machine Learning Intuition: Cost Function Optimization

Suppose you want to identify and describe the patterns that arise in some data.

How do you look for patterns? How do you measure how closely your descriptions match the data? How can you improve your descriptions  of this data? How do you know your description is general enough to describe other data like this data?

Continue reading “Machine Learning Intuition: Cost Function Optimization”