All right.
Welcome to a brand new module in this module.
We're gonna be building a Caris model from scratch.
Do some image classification.
Previously we've been using a pre trained model.
This time we're gonna train our own model and we're gonna do all of that from beginning to end.
So we're gonna formulate our research question.
We're going to gather our data clean our data and process our data explore and visualize some of the
data and we're going to train our neural network on our local machine.
And finally we're going to evaluate how we're doing a high actually want to kick off this module with
a bit of a story you see in 2015.
A Japanese man called Makoto Koichi kit was helping out his parents on their cucumber farm and one thing
he realized was that his parents were sorting their cucumbers by the cucumbers quality.
So for example a really fresh crispy and prickly cucumber sold at a huge premium to something that's
like lower grade or damaged not all cucumbers are created equal apparently.
However he also learned that farming cucumbers is a lot of work especially sorting cucumbers because
apparently during harvest time his mother would spend about eight hours a day just sorting cucumbers.
So that's when Makoto had an idea.
What if he could use image classification to classify the cucumbers according to their grade.
So like premium normal or lower grade hen then he could put those cucumbers on a conveyor belt and probably
drop them off in the appropriate box.
So this was the task that Makoto set himself the first step was gathering lots of data on his cucumbers.
He started taking pictures of them and he started labelling these pictures of the cucumbers according
to their grade hand.
Makoto spent about three months or so taking about 7000 pictures of these cucumbers and this data set
was what he used to train his algorithm.
First he downsized all these pictures into like 80 pixel by 80 pixel images and then he used to tensor
flow on his own computer to train a neural network and then his computer spent about two days training
the model and calculating the weights.
And after all that was done all he had to do was build some sort of sorting machine which had the cucumbers
coming on a conveyor belt.
They would make a stop in a photo booth where a picture was taken of them and then they moved down the
conveyor and were deposited in the appropriate box.
So this is a real story of a real person using image classification to solve a real problem.
And we're going to take some inspiration from this story.
We're also going to build our own image classifier.
And it's going to classify 10 different things.
It's going to differentiate among dogs ships horses frogs deer cats trucks cars planes and birds.
In total we're actually going to have 10 different classes.
That's the word that you'll see in the literature for the different categories.
And the simple model that we're going to build is going to be an artificial neural network called a
multi-layer perception.
That's the name for it.
It's one of the simplest kinds of neural networks and we're going to see how it fares.
We're gonna see what kind of challenges we come up against and how we can resolve them.
So I hope you're as excited as I am.
I can't wait to see you in the next lesson where we're gonna do the setup.