 So, welcome to the third week of the deep learning course. Let's first talk a little bit about last week. What we did last week is we focused on linear deep learning, not because that's most interesting, but because it's a great way of building intuition. We learned about PyTorch and AutoGrad, the tools that make our life so easy these days. We talked about the abstraction of artificial neural networks and how an artificial neural network actually works is different to the math with which we conceptualize it. We talked about the dynamics of learning and how in linear systems we can often produce a good intuition and how a system will learn. And then in the end, we talked a little bit about properties of high dimensional spaces. Again, much of what we did in the first week, in last week, in the second week, was about getting an intuition for how deep learning systems will work. Now, I want you to go back to your part now and discuss what you learned last week. What are your current questions? What are you currently unclear about? What do you need to learn? What do you like to learn? What do you hope to get out of thinking more than what we did last week? And where would you like to develop an even better intuition? So discuss what you learned and what's still unclear.