Welcome to the third course of this specialization. This course is called Natural Language Processing with Sequence Models. In this course, here are some things you get to build. First, you take sentiment analysis to the next level with deep neural networks. You also build a language generator using RNNs, or recurrent neural networks. You apply LSTM units. LSTM stands for long short-term memory. You apply LSTM units to the problem of named entity recognition. And finally, you use Siamese networks to identify duplicate questions, like say if there's an online discussion forum and different people are asking questions. Can you figure out if two different people ask essentially the same question but with different wording? With the skills you develop in this course, you'll be able to build powerful NLP systems to solve problems across a wide range of industries. It is my pleasure to welcome Lucas and Eunice as your instructors for this course, and they are thrilled to dive into these topics with you. Eunice, perhaps you could say a bit more about the applications that learners will build in this course. Sure thing. Thanks, Andrew. Well, I'd like to start by saying that in the first two courses of this specialization, you built a powerful foundation that will provide you with both the context and the fundamental skills you need to tackle this course. For example, you've already done sentiment analysis in course one with a simple naive Bayes classifier. But now, you will leverage the power of deep neural networks to build a much more robust sentiment analysis classifier. You've also seen how to do things like predict the next word in a sequence using relatively simple n-gram language models in course two. In this course, you'll create an advanced model using recurrent neural networks to generate texts. You can think of this course as taking the step from foundational skills into building real-world NLP applications. Very cool, Eunice. I think that's a great way to think about it. Lucas, maybe you could say a bit more about the applications learners will get to build. Sure. Thank you, Andrew. Well, as you saw in the first course, the problem of sentiment analysis is a really tricky one. But in many applications, you want to determine the sentiment of a sentence, so it's a really good problem to work on as well. With language modeling, the problems you can solve are almost infinite, from translation to autocomplete to generating text from scratch. In week two of this course, you'll work on named entity recognition, which is the problem of separating named entities in sentences like people and places. This is a building block of many important NLP systems. Finally, in week three of this course, you will tackle the problem of identifying duplicates. The question whether two pieces of text are duplicates of each other might not sound very interesting at first, but as you'll see, it's actually a core building block of things like online forums and search engines, and we'll show you how to solve it. So we're really excited to show you these applications and bring your skills to the next level. Thank you, Lucas, and thank you, Eunice. This is going to be an exciting course, so let's get started. Good luck! Have fun!