Quickly build a python environment for deep learning and data science (Windows)

environment

OS: Windows 10 (Surface Pro4） No GPU

procedure

--Download and install Miniconda from the following site - https://conda.io/miniconda.html --Use Miniconda3-latest-Windows-x86_64.exe --As of 06/10/2018, conda4.5.4 and python3.6.5 are installed by default. --Create a virtual environment for deep learning with conda and install the necessary modules --Launch Anaconda Prompt from the Start Menu --Create virtual environment: conda create -n env_name python = 3.5 --Create with the above command assuming python3.5, which has many library support --Virtual environment activation: ʻactivate env_name --Installation of required modules: Many modules can be installed from each conda pip, but priority is given to conda --From conda:conda install numpy scipy sympy pandas scikit-learn matplotlib seaborn pillow ipython cython pytest jupyter m2w64-toolchain Werkzeug pyyaml tensorflow keras --Install from pip:pip install ipdb` --Operation check

Creating a project with pycharm

--Select Create New Project and create a project in any location and with any name. --If you are asked for Project Interpreter at the time of creation, specify (install_path) \ Miniconda3 \ envs \ env_name \ python.exe --Changes after creation: From File> Settings> Project: project_name> Project Interpreter

update

--Update of what you installed with conda: conda update --all --Update what you installed with pip: pip install module_name -u

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