For new students (Recommended efforts for Python beginners Part 1)

What I've learned from the beginning of Python

Introduction (author's present)

I am currently conducting research on deep learning in the first semester of the first year of graduate school. In the research, features are calculated using CT image data and PLY data, which is the processed data, and learned by a convolutional neural network. Finally, aiming at automatic diagnosis of fractures on the bone surface, we are making rapid progress every day. I will. There is a treatise of the first book, so if you are interested, please read it. (If you have any comments about the treatise, please (´; ω; `)) https://ieeexplore.ieee.org/abstract/document/9231453

Motivation for writing

To explain why I decided to write this article, I will use this article as a reference when using Python in case a new student comes in while two years have passed since I started using Python. This is because I thought that I would like you to refer to the tacit knowledge part. (Actually, I felt that the waiting time of the program currently running seems to be quite long)

My recommended Python tool

First of all, I don't think you need to wonder which tool to use when you start using Python. I would like to explain it step by step, but I think that you should install it once for reference and get used to it while using it. For the preferences that beginners will probably be most stumbled upon, I think you should download ** Anaconda ** first. You should be able to go without problems if you download while searching for this download part. (Reference URL1) https://sukkiri.jp/technologies/ides/anaconda-win_install.html

A list of tools used in Python will appear like this. It's convenient, isn't it? (No, I don't know anything yet) Of these, the tool recommended for beginners is overwhelmingly ** Jupyter Notebook **. Let's press Launch at once. (Usage is omitted) (Reference URL2) https://www.javadrive.jp/python/jupyter-notebook/ Aside from the fact that you don't understand the meaning when you look back at the execution code, Jupyter Notebook is kind to beginners to be able to execute each block (cell) like this (personal thought). The first thing I did here was to check what kind of behavior it would take by trial and error while executing it one by one. This is recommended if you want to try a short code such as a reference book. In addition, there are multiple shortcut keys in Jupyter Notebook. I'm going to explain it later, but I think it's better to get used to it in the early days because shortcut keys are indispensable when executing at explosive speed and trial and error. Click on the far right at the top of the Jupyter Notebook for more details. Personally, I think you should be aware that you can switch the cell command input with the execution command (Shift + Enter) and Esc or Enter at the beginning. In addition, what I wanted to know in the early days was how to comment out. I will explain with the above command. Suppose you want to delete the middle line of these three lines. As for how I do it now, click the ‘a = 0’ part three times to select one line, and then comment it out with the Ctr +'/' command. What happens if you run it with this? You can see that the code in the middle has been disabled. You can also see that if you repeat the same operation, you can go back and execute it. There is no doubt that it will be convenient later! This can also be used with Pycharm, which will be explained later, so for your reference, It is ant to learn from Jupyter Notebook as it is, but it is difficult to check where the error occurs when executing a sample code or a long execution statement. Also, even if you think that you have specified the same variable name, if even one character is wrong (same for tabs, commas, double quotation marks, etc.), there is no objective point of view and it is difficult to notice. So when you start writing long code, ** Pycharm ** is recommended. I think that there is no problem if you refer to the following for the installation method and development method of the desktop version. I specify Python included in Anaconda for this Interpreter part, but I think that you should think about it each time you have trouble building the environment because you can mess with it after you have trouble. By the way, I recently noticed that it is also included in Anaconda Navigater. https://gammasoft.jp/python/pycharm-install-on-windows/ As for Pycharm, it seems to be long, so I will close it here. I was keenly aware that I still do not have the power to summarize the explanation in a straightforward manner, so I will do my best through trial and error without discipline.