In the first course, you learned how to use TensorFlow to implement a basic neural network, going up all the way to a basic convolutional neural network. In this second course, you go much further. In the first week, you take the ideas you've learned and apply them to a much bigger dataset of cats versus dogs on Kaggle. Yeah, so we take the full Kaggle dataset of 25,000 cats versus dogs images. In the last module, we looked at horses and humans, which was about 1,000 images. So we want to take a look at what it's like to train a much larger dataset. And that was like a data science challenge not that long ago. And now we're going to be learning that here, which I think is really cool. Yeah, and in fact, with substantially similar ideas as their previous course, and apply it to a much bigger dataset, and hopefully get great results. Yeah, we're hoping for good results. Let's see what the students get as they do some of the assignments with it as well. And one of the things that working with a larger dataset then helps with is overfitting. Right, so with a smaller dataset, you are at great risk of overfitting. With a larger dataset, then you have less risk of overfitting. But overfitting can still happen. And then in week two, you learned another method for dealing with overfitting, which is that TensorFlow provides very easy-to-use tools for data augmentation, where you can, for example, take a picture of a cat. And if you take the mirror image of the picture of a cat, it still looks like a cat. So why not do that and throw that into the training set? Exactly, or for example, you might only have upright pictures of cats, but if the cat's lying down or it's on its side, then one of the things you can do is rotate the image, right? So it's like part of the image augmentation is rotation, skewing, flipping, moving it around the frame, those kind of things. And one of the things I find really neat about it is, particularly if you're using a large dataset, and a large public dataset, is then you flow all the images off directory, and the augmentation happens as it's flowing, so you're not editing the images themselves directly. You're not changing the dataset, it all just happens in memory. So then, one of the other strategies, of course, for avoiding overfitting is to use existing models, right, and to have transfer learning. Yeah, so almost, I don't think anyone has as much data as they wish for the problems we really care about, so transfer learning lets you download a neural network that maybe someone else has trained on a million images, or even more than a million images. So take an Inception network that someone else has trained, download those parameters, and use that to bootstrap your own learning process, maybe with a smaller dataset. Exactly, and that has been able to spot features that you may not have been able to spot in your dataset, so why not be able to take advantage of that and speed up training yours? Which I particularly find that one interesting as you move forward, that to be able to build off of other people's work, the open nature of the AI community, I find, is really exciting, and that allows you to really take advantage of that and be a part of the community. Standing on the shoulders of giants, and I use transfer learning all the time, so TensorFlow lets you do that easily, especially open source. And then, finally, in the fourth week, multi-class learning, rather than doing two classes, like horses as humans or cats as dogs, what if you have more than two classes, like classify rock, paper, scissors, that would be three classes, or Inception would be a thousand classes. Yeah, so the techniques of moving from two to more than two, be it three or be it a thousand, are very, very similar, so we're going to look at those techniques and we'll look at the code for that, and we have a rock, paper, scissors example that you're going to be able to build off of. So, in this second course, you take what you learned in the first course, but go much, much, much deeper. And one last fun thing, Lawrence has seen this coffee mug in the AI for Everyone course, and he asked me to bring it. I love that course, so thank you so much. It's a great course because it's got everything for people who are beginning, even people who are non-technical, all the way up to experts, so thank you for the mug. But is it okay if I say I spotted a sports car in the course as well? Would you bring that? Yeah, I don't have one of those to bring to you. So, I'm really excited about this course. Please go ahead and dive into the first of the materials for week one.