Discussing the article: "Neural Networks in Practice: Gradient Descent"

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Check out the new article: Neural Networks in Practice: Gradient Descent.

In this article, I will try to explain as simply and clearly as possible one of the most controversial issues that arises when discussing neural networks: how to find the best possible point, or the minimum of the loss function. I'll show you the difference between linear regression and gradient descent. Both methods are fairly simple and serve as examples of how the path that seems obvious isn't always the best one.

Well, in this article, we're going to do something a little out of the ordinary. I know that many people think my articles on neural networks form a series, although in reality it's impossible to create a series of articles on this topic. Believe me: as strange as it may sound, it's practically impossible to write a series of articles about neural networks. This is because we have to return to the basics time and time again. And the reason is simple: HUMANITY HAS NO CLEAR IDEA OF HOW TO CREATE SOMETHING LIKE THAT.

What I just said may seem completely illogical, since, apparently, something new on this topic comes up every day. In practice, however, things aren't quite like that. In reality, what happens is this: we find ourselves having to go back to what we’ve already done, time and time again, and rethink it as we try to solve some new problem that has arisen.

Author: Daniel Jose