So in this lecture, we'll be introducing the next section of this course, which is all about the artificial
neuron and how to implement one in TensorFlow.
The main goal of this section is to learn the basics of TensorFlow so that you can carry this knowledge
forward in the later sections on and CNN's intranet.
Basically, what we'll end up doing in this section is revisiting an old topic, which is logistic regression.
But from a new perspective, this new perspective is that this model is actually a model of the neuron,
and obviously a neuron is the basic building block of neural networks.
In addition, we previously didn't discuss how logistic regression actually learns from the dataset
we give it.
So this section will look a little deeper into not just how the model makes predictions, but also how
we train the model before we make those predictions.
To give you a quick outline for this section, we'll begin by looking at the TensorFlow syntax for simply
fitting a line.
I'll assume that you've all encountered the concept of a line of best fit in high school so that this
needs no introduction.
This will introduce us to the very basics of TensorFlow syntax.
The next step will be to look at how we can use the same syntax to implement logistic regression, which
is another linear model, but for predicting categories instead of continuous values.
Once we've done that, we'll go a little more in-depth into why this model is a model of the neuron
and how this neuron learns.
In other words, we'll talk a little bit about what goes on when we call model does fit instead of treating
it like a magic box that does what we need it to do.
This is important actually, because in deep learning, sometimes it does not do what we needed to do.
This is unlike models in Typekit learn, which work pretty much most of the time in deep learning.
It's necessary to monitor your model's progress to make sure things are going as expected.
In other words, you cannot just trust that just calling model does fit will do what you want.