00:00associated input Xi. Now, this is given to a formula W nu is equal to W old plus change
00:08in this weight delta W. So, that becomes a new weights for the next input of the next
00:15epoch. So, this is what is the total summary of a single layer perceptron.
00:22So, with this we have come to an end of a theory session. Now, let us look into a simple
00:29implementation
00:30of a single layer perceptron. For this let us take an example of the problem that we
00:36had solved in one of our session. We will take the same input and then we will see how
00:41we can implement a single layer perceptron. And since we are using Google collab to implement
00:47it, let us first see some of the basic concepts of Google collab and then we will get into
00:54the implementation of it. First step is go to Google and then log into your Gmail account.
01:01Here I have already logged into my Gmail account. And the very first step give Google collab,
01:11give Google collab and then go to the first link here welcome to collab. So, here you will be
01:19you will get this particular window and here you can see at the bottom new notebook click
01:25on this new notebook. It will take few seconds for you to open this. So, this is what is a
01:36Google collab notebook. Now, if you look into this the way on the top you can able to change
01:43this and then give since it is an first program that you are learning, you can give any of your
01:50file name here. Now, if you look into this most of you will all will be familiarized with python.
01:58Whenever you write a python program, you will be saving a program with dot py. But here if you
02:04look into we are saving the program by default with the extension ipynb, we are saving a program
02:12with ipynb. What is this ipynb? It is an interactive interactive, I stands for interactive, py stands
02:20for python and nb stands for notebook. Now, why is that it is called as an interactive python
02:27notebook is. Now, whenever you are working on a Google collab, it is just not a python program
02:34that you will be writing, you will have some rich text elements also. What do you mean by rich text
02:41element? Now, for example, here we are writing a simple a is equal to 5, then we write b is
02:49equal
02:49to 3 and then we will go for simple a plus b and then we will print c. This is
03:03a simple program that
03:06we have written. Now, when we execute this particular cell, we will be able to see the output. So, let
03:15it
03:15take time. Once you execute this, you will be able to see the output. Now, you can see there are
03:26so many
03:27you know at the top you have a menu bar and the menu bar has the file, you have an
03:35edit, we have a view,
03:36we have a you know insert, run time, tools and help. Now, when you go to a files, you can
03:42able to see
03:43all of these options. Now, you can able to save this, you can save a copy in your drive. So,
03:50that
03:50you know you will not be losing it, you can if at all if you have a github account, you
03:55can save a copy
03:56into your github also. And you can open if at all if you already have some file, you can open
04:03a new
04:03notebook. If you have some other notebooks, you want to upload that, you can use this option.
04:10So, as and when we go through this course, we will be exploring each one of this option. Now,
04:15you can able to see that there is an output that is being displayed. Now, you have a code here
04:21and
04:21we have a text. So, I click on text so that the text is displayed. Here, I am going to
04:27write something.
04:42This is a simple addition of two numbers program. So, I have given some description about this,
04:49I made it as bold and then we are running this. How did we run? I have pressed shift enter.
04:57So,
04:57here I have used shift enter. Now, when you look into this particular you know when you view this,
05:06you can able to see there is some text that is written, there is a program that you have written
05:11and you can able to see the output also here only. So, it is more of an interactive python notebook
05:19rather than just writing only a python code, later when you run you see an output differently.
05:24Instead of the instead of writing just a python program, you can able to view everything very
05:30clearly here. So, this is all about you know some of the basics that are required for you.
05:36Now, let us understand what exactly is Google Colab. Now, why is that we require to use this Google
05:43Colab? Google Colab, it is a free cloud based you know Jupiter notebook and it is a service which
05:50is provided by Google and here you can execute the python program without any explicit installations
05:57that are required. And Google Colab is something which is especially useful when you are dealing
06:03with machine learning and deep learning. Now, why it is very important to use Google Colab when you
06:09are dealing with machine learning and deep learning is, you can see in the runtime, we have something
06:14called as change runtime type. When you click on change runtime type, you have a provision to use GPU as
06:22well as you have a provision to use even TPU also. So, these are high processing units which is very
06:31much
06:31useful for us when we are working on real time large amount of data and by default it is CPU.
06:38In case,
06:39if you want to change you can select any one of this and then you can click on save and
06:46you have to do
06:46all these things before you start doing your before you start writing your code, you can first go change
06:53the runtime type and then save whichever accelerator that you require as per your problem statement and
07:00click on save. So, this is a simple you know basics that are required for you. Now, here you can
07:07see
07:07how much you know what is the configuration that is given to you, you can able to see here the
07:13RAM and
07:14so much of RAM that is allotted and this much of this.
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