Welcome to the final course in the PyTorch Professional Certificate, Advanced Architectures and Deployments with PyTorch. In the previous two courses, you learned the fundamentals about building and optimizing models that can tackle real problems. In this course, you learned about creative ways to put together a new network of building blocks in PyTorch to build advanced architectures like you hear about the Siamese net, ResNet, DenseNet. And these more advanced architectures are great for specialized tasks like, you see, deciding if two signatures match or if two pictures of faces are of the same person. So when you finish this professional certificate, you'll be ready to tackle with a fairly cutting-edge way many of the practical challenges that businesses are facing today. So there's four modules. In the first one, we're going to look at the custom architectures that Andrew was talking about, models that can branch, share information, and even adapt their behavior. Along the way, we'll look at the Siamese networks, ResNet, and DenseNet. Then in module two is where I'm geeking out, and we're going to focus on vision. And we'll look at the techniques that really reveal what parts of an image a model is focusing on. And we'll experiment with some of those same ideas. Then in module three is the big one. We'll move on to language and we'll look at the transformer architecture itself, the encoder, the decoder, and the encoder-decoder, and all of the attention mechanisms. In module four, we're just going to pivot in how to prepare your models for deployment. The things that you do, for example, to compress them and quantize them and the trade-offs that you have to make in doing that, and in making them efficient enough to serve real users. With this final course of this professional certificate, I think you come away able to get really much closer to the cutting edge and tackle practical problems that very few teams in the world even knew how to do just one or two years ago, especially with the open source offerings that you now take advantage of with this skill set. And so to get started in the next video, Lawrence will show you why the sequential architectures that you've learned about so far sometimes has limits, but also how PyTorch gives you flexibility to build architectures that solve problems using a more complex architecture. So let's head over to the first video and get started.