So, what stochastic depth is doing is it tries to ignore adding identity or adding f of x sometimes to the end of the result. Okay, let me clearly explain. As you can see this, let's say the vertical lines are layers and the number above the vertical lines is probability that you will add the residual to the output. So, what stochastic depth says is, let's say there's a probability, I'll draw a unique number. If my unique number is lesser than the probability, I'll add my residual to the output of the layer. So, how does this help? One, one and a direct help is regularization. So, if you keep on training your net continuously over the images, it starts overfitting to the image. So, doing like what every other method which says drop a parameter or anything else does, dropping a layer also helps you with regularization and what else? It helps you with the speed as well. Why? So, when you're picking and choosing which layer to run or when to run a connet, you're ignoring running connet certain times also. You're just sending the identity from previous function through and you're not running the residual. So, from the computation around 70 percent, you're only running connet 70 percent of the time if you're assigning dk probability to a layer. So, this, so, so, one direct output of it is accuracy. You can see that till 400 epochs residual networks without stochastic depth and weak stochastic depth are performing same. So, after 400 epochs what is happening? The regularization part of the advantage comes in handy and the rest nets with stochastic depth give a greater accuracy and what else? As I told speed up, you can see almost, so, I'm giving test errors on y-axis and survival probability, the numbers above the vertical lines on x-axis by choosing what kind of a test error you want and what kind of survival probability you want for the layers. You can see that you can get a speed up up to 2 x. So, why the speed up? Still, we are able to train it in one week, why the speed up? Let's say tomorrow you want to run a, ResNet which is 1200 layers deep. So, 1200 layers deep is the difference between months to couple of weeks. Probably it takes you to run a 1200 layer ResNet for 4 weeks and stochastic depth can run it just in 2 weeks. So, boring graph on vanishing gradient if you actually care about that and other methods which I want to leave you with. This because of couple of minutes I will try to, I'm not doing justice to the methods but I will try to explain what they are. The recent past they have published something called a swap out method. A swap out method is also a regularization method where they add residual, feed forward and drop out as well. Fractal nets which are hyped called ultra deep neural nets which say that we do not need residuals by stacking these fractal nets together will still give a output. And residual the third one which says that instead of just adding the residual from the previous layer you can add a ensemble of previous layers residual also to the current layer and give you the output. So, last one yeah. So, I've done implementation of the stochastic depth method and residual learning it's on my GitHub page. That's it. Okay. Thank you. Oh. Yeah. Okay. Thank you. Oh, yeah. Okay. Thank you. Oh, yeah. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Hi. This is the lip. I had two questions. One, why should this add the residue help? And two, in one of the graphs that you showed, maybe around 200 epochs after that was a sudden drop. Why was there a sudden drop? Because the sudden drop is because of the change of learning rate. The unusual method, what you do is you write a call back for changing your rate of learning after changing some certain number of epochs. So that was the sudden drop. And intuition behind residual net. Like, it's still the risk of stoning stupid. Deep learning is still a very black box method. But if you ask my intuition, it's like you're adding the same method. So you're adding the state of a previous layer to give the context to the current input. So this will help with the gradient cost back propagation method. You need to have a context of the whole thing. And you're optimizing the whole function. Hello. How close is the LST? Sir, can you speak up please? How close is your LSTM analogy? I mean, your gut feeling on how close is the LSTM analogy? Okay. It was, it's widely discussed on Reddit. If Reddit is not a very credible source of discussions, there is a publication by MIT PSD students, which draws a close analogy between residual nets and RNNs. So I could offline give you the paper as well if you like it. So yeah, thank you.