I don’t quite understand the bolded part and I noticed that when you matrix multiply
the user weights with user dummies , the user activations ( green color portion ) is the same. Is there any link to the bolded section.
https://course.fast.ai/videos/?lesson=5 ( around 25mins )
Well, here’s the thing. This input, we claim, is this one hot encoded version of user ID number 1, and these activations are the activations for user ID number one. Why is that? Because if you think about it, the matrix multiplication between a one hot encoded vector and some matrix is actually going to
find the Nth row of that matrix when the one is in position N. So what we’ve done here is we’ve actually got a matrix multiply that is creating these output activations. But it’s doing it in a very interesting way - it’s effectively finding a particular row in the input matrix.
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# Lesson 5
[Video](https://youtu.be/uQtTwhpv7Ew) / [Lesson Forum](https://forums.fast.ai/t/lesson-5-official-resources-and-updates/30863)
Welcome everybody to lesson 5. And so we have officially peaked, and everything is down hill here from here as of halfway through the last lesson.
We started with computer vision because it's the most mature out-of-the-box ready to use deep learning application. It's something which if you're not using deep learning, you won't be getting good results. So the difference, hopefully, between not during lesson one versus doing lesson one, you've gained a new capability you didn't have before. And you kind of get to see a lot of the tradecraft of training and effective neural net.
So then we moved into NLP because text is another one which you really can't do really well without deep learning generally speaking. It's just got to the point where it works pretty well now. In fact, the New York Times just featured an article about the latest advances in deep learning for text yesterday and talked quite a lot about the work that we've done in that area along with Open AI, Google, and Allen Institute of artificial intelligence.
Then we've kind of finished our application journey with tabula and collaborative filtering, partly because tabular and collaborative filtering are things that you can still do pretty well without deep learning. So it's not such a big step. It's not a whole new thing that you could do that you couldn't used to do. And also because we're going to try to get to a point where we understand pretty much every line of code and the implementations of these things, and the implementations of those things is much less intricate than vision and NLP. So as we come down this other side of the journey which is all the stuff we've just done, how does it actually work by starting where we just ended which is starting with collaborative filtering and then tabular data. We're going to be able to see what all those lines of code do by the end of today's lesson. That's our goal.
Particularly this lesson, you should not expect to come away knowing how to do applications you couldn't do before. But instead, you should have a better understanding of how we've actually been solving the applications we've seen so far. Particularly we're going to understand a lot more about regularization which is how we go about managing over versus under fitting. So hopefully you can use some of the tools from this lesson to go back to your previous projects and get a little bit more performance, or handle models where previously maybe you felt like your data was not enough, or maybe you were underfitting and so forth. It's also going to lay the groundwork for understanding convolutional neural networks and recurrent neural networks that we will do deep dives into in the next two lessons. As we do that, we're also going to look at some new applications﹣two new vision and NLP applications.
### Review of last week [[3:32](https://youtu.be/uQtTwhpv7Ew?t=212)]
Let's start where we left off last week. Do you remember this picture?