![]() ![]() Then run the installer, and finally start the Anaconda Navigator. If you're using Windows or Linux, make sure to pick the 64 bit installer if you have a 64 bit system. If you don't know, take the python 3.X version Please make sure to chose the version specified in the tutorial you want to run. : the web server that will allow you to follow this tutorial and run the code directly in your web browser.Ĭhoose either the python 3.X or the python 2.X version. : visualization tools, essential to check what we are doing : core package providing powerful tools to manipulate data arrays, such as our digit images It will provide an easy access to the handwritten digits dataset, and allow us to define and train our neural network in a few lines of code ![]() : one of leading machine-learning toolkits for python. In a nutshell, the anaconda team maintains a repository of more than 1400 data science packages, all compatible, and provides tools to install a version of python and these packages at the push of a button, and under five minutes. Standard for developing, testing, and training on a single machine. Machine learning on Linux, Windows, and Mac OS X. Is the fastest and easiest way to do Python and R data science and With over 6 million users, the open source Anaconda Distribution Graphical installation of Anaconda: recommended if you are new to Anaconda.Ĭommand line installation of Miniconda: recommended if you want a fast and light install, and if you want to work with the command line. I'll present you with two installation methods for Anaconda: So to make it easier for me and, most importantly, safer for you, I'm summarizing the instructions in this short post, and I will refer to it from now on. The environment construction method with Miniconda is summarized below.I've realized that I'm explaining how to install Anaconda over and over again in most of my posts, often messing up with the instructions! I first built the environment with Anaconda, but I couldn't grasp the contents, so I uninstalled it and rebuilt it with Miniconda.Īlthough Anaconda is standard and rich in tools, you end up having to look into the package when you write your own programs.I think it's important that you know what's in it. People who don't like installing unnecessary packages.People who want to know which package they are using.Those who want to start machine learning as soon as possible.People who don't care if there are unnecessary packages.People who do not want to have a hard time building an environment.Which one should build the environment Suitable for Anaconda Installation of python is easy, but necessary packages and execution environment are built individually using conda. The smallest configuration version of Anaconda. Graphical User Interface (GUI): Anaconda Navigator.Integrated Development Environment (IDE): Jupyter, JupyterLab, Spyder, RStudio.Package: numpy, pandas, Matplotlib, Scikit-learn, Tensorflow.If you install Anaconda, you will be able to use packages for scientific calculation and data science together with Python.It also includes "R", a programming language for data science alongside Python, and their comprehensive development environment.Roughly speaking, the following applications are installed. "Python + R language + conda + 1000 or more related packages + execution environment + etc. It's true that Anaconda makes it easy to build an environment, but it also has its disadvantages.Therefore, I compared the characteristics of Anaconda and Miniconda. When it comes to building a machine learning environment with python, many books and sites say that you should use Anaconda for the time being. ![]()
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