So, it is easy to notice that with b 0, the function will always pass through the origin [0,0]. And when we introduced values to b keeping a fixed, the new functions will always be parallel to each other. So, what could we learn from it? We can say that a component determines the angulation of the function, while the b component determines where the function cuts the x-axis. I think you already noticed the problem in that, right?

When you want a person to do something, you train them. When you want a computer to do something, you program it. However, there are ways to make computers learn, at least in some situations. One technique that makes this possible is the perceptron learning algorithm. A perceptron is a computer simulation of a nerve, and there are various ways to change the perceptron's behavior based on either example data or a method to determine how good (or bad) some outcome is.

When you want a person to do something, you train them. When you want a computer to do something, you program it. However, there are ways to make computers learn, at least in some situations. One technique that makes this possible is the perceptron learning algorithm. A perceptron is a computer simulation of a nerve, and there are various ways to change the perceptron's behavior based on either example data or a method to determine how good (or bad) some outcome is.

Welcome to the second lesson in our Practicing Deep Learning Series. Thoughtly is writing a multi-part tutorial series focused on understanding the foundations of Deep Learning, specifically as they apply to Natural Language Processing. If you want to jump to another post check the post listing here. Last time we focused on the elements of a simple single neuron network. We specifically discussed those that feed into the neuron – the inputs and weights – and their interaction via the dot product.