In this lesson, we're going to go over all the courses in this specialization, as you know there are six courses. You're watching Course 1. So, let me kind of go over what's in each of these courses. Let me switch the slides here. Course 2, this is the Introduction to DragonBoard and the 96Boards ecosystem. We'll show you all about waiting at the DragonBoard, unpacking, installing the things you need, and setting up, so it's all DragonBoard oriented. This form is kind of the core of the specialization. We'll also talk about the 96Boards specs to get the most of the on processors, so it's a pretty detailed course. Course 3, we'll kind of move away from the DragonBoard a bit, it's about the Cloud Services. Very soon you'll hit maximum processing power off the DragonBoard, you can't do it on the board you have to off load processing to the cloud, so we have chosen Amazon Cloud Services as to follow along in this course, you're welcome to use other services. Course 3 we're going to cover AWS. How to set up on Amazon, what are the services offered in Amazon, all the image processing services. In particular, we're going to cover the Amazon IoT SDK kit. There are two parts to the kit. One is the server kit and there is the client kit, and you'll do a few projects in this specialization using the Amazon IoT SDK. So that's course by itself where it's not, we don't do too much on DragonBoard in Course 3 but it's a very useful course by itself, it will get you up and running with Cloud Services. So now, let's move to Course 4. Course 4 and 5 got plenty of material, two and three form the backbone of the specialization. If you have finished two and three you'll be firing on all cylinders. You've got all the kind of basics you need to kind of build your own IoT. Three and four form offer some advanced concepts, in particular Course 4, communication. We do a lot of audio and MIDI. MIDI stands for music instrument digital interface. It's a protocol invented to kind of make musical instruments communicate with each other in an easy fashion, like computers and all that. So we'll talk a lot about processing music signals in Course 4, using the DragonBoard. Course 4 also forms the backbone of Capstone 1. We'll come to what Capstone 1 is a little bit later but we offer you all the fundamentals you need to deal with Capstone 1, if you want to follow along. Course 5 there's a bunch of stuff here. It's multimedia in particular, a lot about image processing and computer vision. Some of the concepts we cover have to do with 2D signals. Course 4 was mainly time series signals. Course 5 deals with 2D signals like images. We'll talk about sensing 2D signals of thd DragonBoard. We'll also be using the Arduino GPU and Diggler computer vision also on DragonBoard, we'll talk about how to use OpenCV and we'll also touch upon deep learning on DragonBoard using Google TensorFlow. At a higher level Course 4 is music related, Course 5 image computer vision related, so you can deal with a lot of image processing concepts in Course 5. Finally, Course 6, Capstone. We got two sample Capstones out there for you pretty small Capstones. You don't of course have to follow what we offer there but it kind of gives you the basics of what you learned in four and five in particular. Capstone 1 is about sonification of sound. What it is, is you are in front of a camera and it's kind of processing an image and you can control qualities of your music using your hands. You flick, for example, you can do this to kind of bring the volume up or bend the page, and it ties into the MIDI concept you learned in Course 4. It also ties into the concepts in Course 5, how to process images. That's Capstone 1. Capstone 2 is using Cloud processing. In particular, we are going to use the Amazon IoT SDK. It's about getting signals from various sensors and these signals are flowing to the cloud from the DragonBoard and you process those signals in the cloud and then make some decisions based on that. While we have chosen is biometrics, not biometrics, I should say, EKG signals like heartbeats, other signals which we're not actually collecting with a real EKG but we have a database of signals stored from real machines. We're going to stream it from the DragonBoard to the cloud and in the cloud AWS we're going to process these using readily available SDKs, which makes inference from these time series signals. For example, it can analyze your EKG, see if you have any existing conditions like bundle branch block or other kinds of abnormalities and it can give you the results in real time. So we got two different flavors here so 2 is about using an Amazon IoT SDK. It kind of fits in if you have projects where you have a lot of sensors, data flowing to the server, to the cloud, and we use the Amazon IoT SDK. Capstone 1 has a different take, image processing, music signal processing, and you can take ideas from these two Capstones and do your own. We'll release all these to give you ideas about how it's done and that kind of summarizes all the courses in this specialization. Thank you.