And it will affect the model more. You can apply mental heuristics to about any problem you want And we talked about in the last lecture about P versus NP problems which MP problems are much more difficult to solve them p problems And we also talked about the size of the problem. We go through each element off the child and for each element, we create around them. The minimum value would be zero. Now we're going to only do it for the why warm the heating load. This lecture will talk about optimization. This is, of course, if the probability is less than you want to mutate. We started stocking mu tinted child's. So you will keep on descending until you find the location where you are A zero. Support Vector Machine Optimization #3: welcome back. 14. Sometimes your little can have several hidden layers, and with this, it started coming to be called deep, like deep neural networks. I finished my B.S. So this index would be minus one minus two, minus three minus four and so on. Which is the decoded X three on the momentum, which is decoded. So the more weights you give the algorithm to find, the more the exponentially it will grow. Ah house with four bedrooms. But because this is an example of a maximization problem 91 So you get your first parents. Try waiting a minute or two and then reload. Because this is length of nine, you start a zero for two to the power off L minus one. So since it took the first and second so zero and one, which is the first and second you can see here and here is equal to this and this. Imagine if you had 100 weights you want to optimize. Optimization Toolbox, SECOND column, Third column four counts. So the terminology is that you need to know something called crossover mutation Philip Chism, Fitness value and selection for cross over. Support Vector Machine, a multi layer perception neural network where we'll use it on a regression problem where we will predict the cooling and heating loads off buildings based on several independent variables or of you call them features. So what it does is it uses the slope of the function, the radiant. /S /Transparency It, based on the previous move, it adopts and change its next small, based on the previous move on its own environment around it. Anderson will become Adam. And why, with why one only So x, X and X eight or discrete variables. The first thing will cover is machine learning just as a brief introduction. Feature Selection #1: Hi, everyone. You know, this times to the power of zero Plus this times two to the power of warrantless. And it also took the last two features. So you have fitness value of 80 for example. Multilayer Perceptron Neural Network Optimization #3: Welcome back. The beautiful thing about neural networks and which distinguishes it from many other algorithms, is that new networks are capable of capturing complex, nonlinear models. It's our generation 31 family number 16 uh, 17 now. So for the ah, here, let's go ahead and say you got this is X one. Okay? The selection of each two parents may be by selecting parents sequentially (1-2, 3-4, and so on). So here you apply a prediction model on these eight independent variables to predict the cool the heating load and also apply. Now make your sticks are here sticks But inspired by nature and the beautiful thing about them are that they are not problems specific. Let's decode this so I can show you how you can decode this. No, here. All these would be clear with the next lecture and the lecture after that. Introduction to evolutionary computing. No, no, no. Thank you. Let's say if you had eight hours off sleep 10 hours of study. Now we have for each. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty.

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