In this video clip, I will explain how to use JupyterLab that we installed in the previous video clip. In order to open JupyterLab, as I said before, first click "windows", and then click "all apps". Then you find Anaconda 3. Click it then you have Anaconda navigator. So, always, in order to open JupyterLab you need to first to open Anaconda navigator. A little bit wait, then Anaconda will be ready. In the following video clips, I will not show you this process. With opened JupyterLab I will start coding immediately after starting this class. So, click "launch" it. But before clicking "launch" JupyterLab, we need to first to make a choice for the working environment. Currently, the default choice is base environment. But we are going to keep working on "ml_env" environment. Make a choice, then "ml_env" will be choosen. And if you look at the left side, there are three choices. If you click "Environments", There are all packages installed, installed in "ml_env". This is a way of checking the libraries installed in your working environment. Surely, you can use Anaconda prompt as I explained it while we were installing Anaconda. But in this Anaconda navigator, you can also check what libraries are available in your working environment. Go back "home" and then click. Now confirm if your working environment is "ml_env". It is correct chosen and then launch JupyterLab. Now JupyterLab is working on Chrome browser because I made that choice before. There are a couple of menus provided here. The first one is "File", then "Edit", "View", "Run", "Kernel", "Tabs" and "Settings" and "Help". I'm not going through all the functions provided here. Let me first explain the key functions that we are going to frequently use while we are coding. If you click "File", what you see is "New Jupyter" Notebook, or you can use new console or new terminal, but we are not going to use this kind of a drop-down menu because our choice will be done on this screen. So, I'm just introducing key commands. And if you want, after making a notebook, well, you can export. For example, LATEX format or PDF format, but in order to convert your Jupyter Notebook file into PDF file, you need to install a few tools. So, I would explain that briefly later. So editing if you click "edit", they are deactivated right now because no new notebook is open yet. View, run, kernel, I am going to explain the functions as we are coding. As we are coding, but here "Setting" JupyterLab theme, currently the choice "JupyterLab Light". So it is if you use this setting, as you type, the letters will be black and background is white. But what about you choose JupyterLab dark? That it changes this way. The background is black. Your letters, typed code will be shown in white. As you can see on the left side, if you prefer this setting you make a choice. But for me, I used to use this light setting. So, I prefer to use this one. Now how can you open a new directory, creating new directory? Here's a button plus button, you see here. If you click this one now you can create here, new directory under your user name, user account. So, let's, let me make a new directory "Coursera". Because, this is a Coursera Mooc, I am creating a working directory Coursera. If you click it now the directory moved into a level deeper, from user directory to a Coursera directory. I'm going to put all coding files here from now on. So this is a way of creating Coursera directory. Surely you don't have to use this name. If you prefer to use other directory names, use your own directory name. But, I'm going to put all the coding here. So don't get confused confused with this directory, with working environment. So when we open JupyterLab we are using "ml_env". In that environment, we are doing a kind of project. So, all the coding files for that project in this case, the project is learning Python coding. So, all the Jupyter notebook files will be stored in this directory. So, if this is a kind of new project in your career. Whenever you do a project, you can create separate directory under separate working environment. So this directory is created under the "ml_env" working environment. So then how can we open a notebook? Here, Python three using Python three. If you click this button, you're creating or you're opening new notebook file. If you click this one, you're opening new console. Console is a kind of code editing screen. So, you can use console, but console is for coding without making document. For example, let me show you click it. Then what happens? A console is created here, right? Yeah console and if you're a write code, for example, two plus two, which is four, "2+2", then outcome. Run. Yeah. For implementing control and enter right? It's not working right now. So, click, "Run Cell". Then outcome apeear here. You type "2+2", then outcome returned is "4". This way of coding is based on console. So, your input and output are printed sequentially. But in this case, you cannot make a kind of a document with this one. So, rather than using this one, we are using notebook file. Here also,Terminal, you can use "Terminal" by opening new terminal. If you click this one is more like a kind of prompted that we encountered, already, while we were installing Anaconda. So, don't forget the, all the options right now, simply focus on what we are going to use. So, how can we create the first notebook file? Simply click this one "Python 3". This is the notebook first Jupyter notebook. And the title name is "Untitled" right now and ".ipynb" the file extension is ".ipynb", Interactive Python notebook. Then how can you change a file name? Simply mouse on it. And choose right mouse click. Then here is you can choose, for example, "Download", "Shutdown Kernel" or "Rename". By clicking rename, you can change the name "My First Code", for example. Then, enter. Then automatically on the head, the file the name is changed also. So how can we start our first coding? Probably, we can use the validation process that we used after installing Anaconda. Anaconda prompt, we checked whether TensorFlow was correctly installed or not. Probably, if you remember the code, we can do that type that code the here again. So how we start? [typing]"import Tensorflow as tf" and execute, [press] "shift + enter". Yeah, it works. Then as you see here, the bracket was empty. But right now, it is asterisk[*]. It means that in the process of bringing "TensorFlow" package in, on to working desk. So, it's like a working desk, empty working desk is ready by opening new Jupyter file. Then you are bringing up TensorFlow working tool. So TensorFlow is ready in this working environment. Then how do we create new object? Which is [typing] "hello", we have used it before. [typing] "=tf.constant" and within parenthesis. As you see here, when we used the prompt, simply the left side parenthesis you have to type the right side parenthesis you have to type by yourself. But if you working on this Jupiter notebook, two parenthesis automatically typed. And you typed just the left side parenthesis, then right side parenthesis automatically ready. So you type in a certain string. So, we are using double quotation mark. I just typed one quotation mark. Then, actually maching quotation mark is also typed. So, you type any sentence. For example, [typing] "I like this course very much". So, "hello" contains this text information. So then you, by pressing shift and enter key you already created "hello object", which contains "I like this course very much" text information. Then you need to, you want to print that "hello object". As we did before simply print command offered by Python. [typing] "print(hello)", then what we can see is this one. Tensorflow "tf.Tensor". This is data-type, Tensor. And "shape" information, data type "dtype" is string", and this is the text information contained in this "hello object". Here "b'" is what? Byte information. So, if you want to see exactly the only string input, text information. In that case if you want to increase cell simply press this addition "+" sign. Then new cell is added. Then if you want to print only text information, what command do use? The print function offered by Tensorflow [typing]"tf.print" you are using it then [typing]"hello" then what happens? You can make a choice "shift + enter" or "Ctrl + enter". If you use "control + enter" the cell is executed, but you are staying there, like this. But if you enter again "shift + enter" what happens? New command line new empty cell automatically added, and cursor is blinking in that new cell. So if you use "tf.print" function, it returns exactly only the text information, the "hello object" contains. So, this is a way of doing coding using Jupyter Notebook. And as you implement or execute the cell, the number increases "1, 2, 3, 4, 5". So two times the cell is executed. And this is all I explained. So, it means that TensorFlow is perfectly ready. So, after finishing this course, you can proceed to learn deep learning. And I already explained how to change the name, and because now we open new Jupyter Notebook many tools are activated, so they are in dark black letter. So if you want to use "Export Notebook", probably you use. For example, sometimes you want to convert your Jupyter notebook file into PDF. In that case, you need to install additional programs. I will explain it as soon and because I already explained the basics of JupyterLab usage. Let me close this one. But before closing Jupyter Notebook, you always have to check this button. What is this one? If you look at this one, currently my first code that is operating. It means that even without shutting down this file, if you close this coding environment, the kernel is keep running. So you, if you want to get out of coding process, you need to shut down. Shut down all, or shut down all, probably better to choose that. Yeah. Yeah. Then there's no more, no more working files. Then you can by clicking here at this one, or here, here, shut down. You, already shut down. So, log out. Then close. And then you need to also shut down Anaconda navigator. So quit. And then JupyterLab, you want to quit. And then Anaconda is quitting also. Then it dissapears and you see. So this is, we have done in order to check where the TensorFlow is correctly installed. But we used this code in Jupyter Notebook. And if you want to use, convert Jupyter Notebook file, ".ipynb" into PDF, you can Google it. But anyway, you need to install three more programs "Pandoc, Miktex, and Pyppeteer". So, three files additionally should be installed if you want to convert. "ipynb" into "PDF" file in JupyterLab I haven't done this and I found out it works well. Also if you google it, you can easily find out how to install the three tools. The review question: You can make, and remove directories in JupyterLab? Is it correct, or is it incorrect? The answer is obviously you can! So the answer is "True!".