An environment construction tool (IDE) “Jupyter Notebook” that machinery learning and data science engineers love so much. This time, the alpha version of Jupyer Lab (Jupiter / Laboratory) which was released before was officially released as a beta version again !
Even codexa teams have many members who transferred from traditional notebooks to Jupyter Lab. In this article, we have summarized usability, merit / disadvantage, etc. of a newly added wonderful new function about IDE’s decided version of machine learning “Jupyter Lab”. It is also a recommended development environment tool for beginners who are planning to learn machine learning from now, so please refer.
It is unnecessary to build Jupyter’s environment, it is open to the machine learning introductory tutorial which can be executed online! Why do not you jump into the machine learning world by first learning basic algorithms?
- Coding the least square method and the steepest descent method with Python with scratch (linear regression)
- Outline of logistic regression and knowledge useful for mathematical understanding and practice (logistic regression)
What is Jupyter Lab? About the Jupyter project
First of all is the Jupyter project? about. Jupyter (Read Jupiter) is a project to develop open source interactive computing (Interactive Computing).
This Jupyter project developed Jupyter Notebook. Jupyter Notebook is a coding environment that can be used on a browser, allowing you to share code with colleagues and teams, develop interactive analysis results, and easily integrate and handle large-scale data. It is one of the most popular development environments (IDE) that machinery learning engineers around the world use it because of its ease and ease of use.
Although I have been using it for many years, Jupyter Notebook can illustrate the entire process of data analysis in a simple and clean form, and I think that there is a great merit to further sharing.
Although it is not easy for each team to write code or analyze data, it is not easy to integrate code in each cell in Jupyter Notebook, and each time the output (output result) Because it accompanies it, it is possible to understand even a code written by another person in a very short time with little effort .
As Jupyter Lab introduced this time, development project is proceeding as an IDE to make it possible to perform data scientist and machine learning engineer’s work process integrally and efficiently.
If you are already an active engineer, I think that some IDE (Integrated Development Environment) is used. Very simple, Jupyter Lab will be an IDE for machine learning and data science .
Although it is Jupyter Lab, it is very similar to the conventional Juyter Notebook, but many new features have been added! In this article, I have summarized how to install Jupyter Lab, even merits and demerits!
Installation of Jupyter Lab
Well, first, I will explain how to install Jupyter Lab. Since it is installable on Pip or Anaconda, it is easy to install with either one if you can use either environment.