Important machine learning equations

"Backprop is just a way to compute the gradients of the weights with respect to the cost function, not an algorithm to minimize the cost function wrt. the weights."

What does the word supervised mean? It's when you define a cost function to be the difference between the training data and the model output.

Aka something like (f(x)-y)^2 which is simply the quadratic difference between the result of the model given an input x from the training data and the corresponding label y.

A learning algorithm is an algorithm that produces a model given a cost function and in the case of supervised learning, the cost function is parameterized with the training data.

The most common way to learn a model is to use an optimization algorithm. There are many optimization algorithms that can be used for this. One of the simplest algorithms for the optimization of unconstrained non-linear functions is stochastic gradient descent.

It's popular because it is a first order method. First order methods only use the first partial derivative known as the gradient whose size is equal to the number of parameters. Second order methods converge faster, but they need the Hessian, whose size scales with the square of the to be optimized parameters.

How do you calculate the gradient? Either you calculate each partial derivative individually, or you use the chain rule and work backwards to calculate the complete gradient.

I hope this made it clear that your question is exactly backwards. The referenced blog is about back propagation and unnecessarily mentions supervised learning when it shouldn't have done that and you're the one now sticking with supervised learning even though the comment you're responding to told you exactly why it is inappropriate to call back propagation a supervised learning algorithm.

That book is pretty much what it says on the cover, but can be useful as a reference given it's pretty thorough coverage. Though, in all honesty, I mostly purchased it due to the outrageous title.

0. https://link.springer.com/book/10.1007/978-3-642-32931-9

- bold-face item headers (eg “Practical Significance:”)

- lists of complex descriptors non-technical parts of the writing (“ With theoretical explanations, practical implementations, and visualizations”)

- the cheery, optimistic note that underlines a goal plausibly derived from a prompt. (eg “ Let’s dive into the equations that power this fascinating field!”)