00:00So, now we are finally talking about six steps, which is the iteration or experimentation.
00:09You can see that we have a total six steps framework, which is the problem defined.
00:17You can see all the steps that you can see in the last lecture.
00:23Now, when you have a fairly good result, then you have one option that you can see that you can see the data and model with some experimentation.
00:36So, if I can improve this as far as you can see.
00:39If I can improve this as far as you can see,
00:43you can see the model of two aspects.
00:45Either you can see the accuracy or the computational cost.
00:50I can see the accuracy and computational cost.
00:56So, you can see the two things.
00:59Either you can see the computational cost or the accuracy.
01:03And then you can see the balance, compromise.
01:06So, this is basically the total process.
01:09This is the total process.
01:10In which you have to see the six steps and repeat the process.
01:13And the accuracy of your model of your accuracy.
01:16Now, you have to see that we have a whole framework and different components.
01:23So, this was the framework.
01:25Now, we have to have some tools.
01:28We have to use this framework to implement this framework.
01:35team and learn them.
01:37Which means that we have to run the market.
01:39In which we have,
01:41anaconda,
01:44this is anaconda.
01:45This is anaconda.
01:47I am powiedział,
01:48aaconda.
01:49Anaconda.
01:51Is anaconda.
01:52Anaconda.
01:53Anaconda.
01:55Of course,
01:58the idea is called anaconda.
01:59Which means josimo.
02:03So, I'm going to tell you how to do the tools.
02:05I'm going to tell you how to do the tools.
02:07So, I'm going to tell you how to do the tools.
02:10So, we can see how to do the tools.
02:13Let's make it more short.
02:23Okay, so I have to resize this image.
02:24Now, I'm going to give you the framework.
02:26I apply this tool to do it.
02:29Now, I'm going to break down.
02:31this whole framework, this whole framework, we apply to anaconda, anaconda, and it's a
02:42idea called jupiter notebook, jupiter notebook, okay, this thing is done, now in this
02:52case, the data analysis, evaluation, and featuring, this total work, these three steps, we will
03:02do libraries, which are three libraries, the first is panda, the second is mat, plot, library,
03:14lib, lib, it's called 2, and the third is numpy, which is basically data evaluation, numpy,
03:23this is the next feature, which is modeling, modeling, which is machine learning, which
03:34matters, tensor flow, tensor flow, it's called pi torch, pi t o r c h, and then we have
03:45scikit learn, s k i t l e a r n, so we have to fix it in our class, okay, so we have
03:54tools that we use to enable data visualisation, and we will use this tools for the model,
04:00in 서ed link ली हमने है this tool use करने और यह सारा काम हमने किसमें करना है anaconda good?
04:07बात- चलिये आप interesting सब्सक्राइब को यह बदाता हूं आपने प्रिशान ही हो ना कि याई
04:12इतनी सारी लाइबरेरी, इतना सारे टूल अपलब यह पता नहीं हमें किसे पता चलेगा
04:16we don't need to know everything about everything
04:18You don't need to know everything about everything.
04:24We need to help out our goals to achieve our goals.
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