Hello everyone and welcome to the tents or float project exercise overview lecture.
This lecture will get a brief overview of your tensor flow project.
Let's go ahead and jump to the Jupiter note book to get started.
OK.
Here we are at the project Notebook.
We're going to go ahead and finish off the steep learning section of the course by using the contrib
learn methods that we just learned about on this bank authentication data set from the U.S. repository
.
And if you click on this link it will take you to the UCI repository for the bank authentication data
set.
And this basically just consists of data that's extracted from images that were taken of both genuine
and forged bank note like specimens and you can see here that there is some attribute information there's
variance in skewness of what's known as a wavelet transform image.
And there's also the kurtosis and entropy of that same image.
All right so we're dealing with image data which means it's really well suited for neural networks and
deep learning.
You'll go ahead and grab data a little bit of exploration of it but then we're going to do something
that we didn't do before which is data preparation.
Usually when using a neural network or some sort of deep learning based system it's a good idea to standardize
your data just like we did for Kinnear's neighbors.
So you'll go ahead and run through the standard scaling for this data for practice and then you'll do
a train test split and you'll do the contrib learn method in order to actually create a deep neural
network classifier using a psychic learned type syntax and don't worry if you get a warning here that
do have some updates that are happening.
PI.
OK then you go ahead and evaluate your model and you'll notice that something will stand out to you
.
Hopefully you notice that your model does extremely well.
We've done all that work and we should do it when your model does extremely well the test set you should
for a reality check.
Compare it to another model so it will go ahead and do is as an option you can also do a random force
classifier just as a reality check and make sure that also does really well and they're in the forst
class for also those extremely well which just means that Stadio was really easily separable.
OK.
Hope you enjoy the project.
Coming up next is the walk through of the solutions to this project.
I'll see you at the next lecture