‫All right.
‫So I just want to give you a quick overview of what we're going to be working through in this section.
‫We're going to be using transfer learning and TensorFlow 2.0 to classify different dog breeds.
‫And we're going to finish off with a full blown end to end multiclass classification problem.
‫An example project.
‫And now I've chosen to do it this way because I think the best way to learn anything is to get hands
‫on.
‫And now, of course, I don't expect you to remember everything off my heart that what we cover.
‫What's more important is thinking about the principles of what we're going to go through.
‫So if we come back to our workflow is to remind ourselves if we're choosing a machine learning model,
‫working on some kind of project, what kind of data do we have?
‫Is it structured data?
‫Well, then we should probably choose a model that's more suited to structured data, and we should
‫probably remind ourselves that in machine learning we have to get our structured data into numerical
‫form.
‫So these are the principles that we have to think about.
‫And the same with unstructured data.
‫Hey, if we're working with unstructured data like images, which is what we're going to be working
‫with, we should probably use a deep learning or transfer learning model, right?
‫So like a neural network.
‫And for that we can use TensorFlow or transfer learning with TensorFlow Hub, which is what we're going
‫to work on in this project.
‫And again, our machine learning model, even if it's transfer learning, still can't look at images
‫unless they're in the form of numbers.
‫In in our case, since we're using TensorFlow, they have to be intensives.
‫So these are the principles I want you to start thinking about.
‫And now what are we going to do, as always, when we attack a new project is to set up our project
‫workspace.
‫And now traditionally we've gone through this route, we've had our computer, we've downloaded install
‫a mini Conda, we've started a new project in a project folder, we've put some data in an environment
‫and a Jupyter notebook in there, and then we've got our coding libraries in our environment.
‫And now this is what's going to blow your mind is CoLab takes care of a lot of all of these steps for
‫us, which is what this notebook is built in like.
‫This is Google CoLab, but it looks it looks like a Jupyter Notebook.
‫Now, you might be asking, why didn't we just use CoLab from the start?
‫Well, CoLab is great for working on projects like what we're about to work on.
‫But there are some times where projects that you're working on can't be used with CoLab.
‫So that's where you go through this workflow.
‫Now we will require a CPU, a piece of computer hardware that's really fast at doing numerical computing.
‫So that's why we're choosing to use CoLab, because it's a Jupyter Notebook running on one of Google
‫servers, which gives us access to a free GPU.
‫And we'll see how to set that up in a moment.
‫What are we actually working on?
‫Let's have a quick look.
‫We go to the Kaggle dog breed identification competition.
‫So if we look at here.
‫What we're going to do is determine the breed of a dog in an image.
‫So because we're working with images, it's unstructured data.
‫And so if we have a look at this, we go to data.
‫So if we look at our data sources, what are we going to do?
‫We're going to download label CSV.
‫We're going to download all of these.
‫So we've got test.
‫These are some images to test on and then some images to train on.
‫So if you go to cable dot com slash C slash dog breed ID with hyphens in their slash data, that's where
‫we're going to get our data from.
‫So I'm going to log in here and download the data.
‫So I've just logged in and now I'm going to click download all.
‫Beautiful.
‫So this is going to take a couple of minutes.
‫I'll pause the video until this download is done and then we'll see what we can do with that in a moment.
‫Wonderful.
‫So my download is just finished from from Kaggle.
‫That took a couple of minutes on my internet speed.
‫Now what I'm showing you here, downloading the data is just one option.
‫You could always use the Kaggle API.
‫Now, if you click on this little question mark here, again, the website's just here.
‫This will give you some documentation about how to use a Kaggle API, which works in Google CoLab.
‫However, I've chosen to do it this way because if you want to work on your own custom data set like
‫one that wasn't hosted on Kaggle, this is one way to do it.
‫There is actually if we search our Google colab i o there is an example notebook.
‫Here we go.
‫I'll link this in the resources section.
‫There it is.
‫An example notebook in Google CoLab.
‫And the beautiful thing about Google CoLab examples.
‫So if you Google something and go CoLab, I just googled io.
‫Is that these are all reasonable Jupyter notebooks.
‫Immediately.
‫So this is going to give us a little bit of an idea about how to load external data, because, remember,
‫machine learning is all about getting our data ready.
‫How can we load external data from local drives, drive sheets and cloud storage into our CoLab notebook?
‫So how can we get this zip folder into our colab notebook?
‫This demo will show us, but I'm just going to show you the way that we're going to do it.
‫And so we're going to go to CoLab dot research google.com.
‫You will need a Google account.
‫And so we are going to just start a new Python three notebook.
‫And now again, if you want some examples, there's a whole heap here.
‫Feel free to search them up and check them out in your own time.
‫But we're going to start a new Python three notebook.
‫Beautiful.
‫Look at that.
‫This is kind of similar to what we've been working with with Jupiter.
‫Now there are a few different commands and a few different things that are slightly different to our
‫Jupiter notebook.
‫But the main premise of what we were doing in Jupiter.
‫It comes over to CoLab.
‫So if we want to write code, we can go import pandas as PD shift and enter.
‫Well we could go numpy.
‫Import numpy is np shift and enter.
‫And if we wanted to add a text cell with some markdown, we can go.
‫This is a collab notebook shift and enter c just like a Jupyter notebook.
‫Now there are a few subtle differences and we're going to see that in the next video.
‫So in this one, we just downloaded the data.
‫I've got that in my downloads folder.
‫Now we've seen an example, Google CoLab notebook of how we can load external data into our Google CoLab
‫notebooks.
‫And we've seen what we're working towards this end to end.
‫I've called this project Dog Vision.
‫I like that name because we're trying to identify different dog breeds in images.
‫But without spoiling too much.
‫Start up your own CoLab notebook in Google CoLab and I'll see you in the next video.