Deep Learning environment setup

moshe roth
Aug 9, 2017 · 1 min read

Why?

Last March I started the Deep Learning Nanodegree program Udacity. I took it after I had some practice w Udacity’s Free Machine Learning course and even implemented some classifiers on @Kaggle. I played with regressions, SVM, Decision Trees etc. All this time I knew that eventually I will want to use DL because of its superior performance and amazing use cases I read about. This is how I created my setup:

Start Here

  1. Download Miniconda

2. Install form terminal:

bash Miniconda2-latest-MacOSX-x86_64.sh

3. add conda path to your .bashrc/.zshrc:

export PATH=”~/miniconda2/bin:$PATH”

4. Now, in order to start working with python in your environment, run in terminal:

conda create -n dlnd python=3source activate dlnd #activate conda envconda install numpy matplotlib pandas jupyter notebook bokeh h5py tqdm tensorflow ipykernel -y # install packages for this env aloneipython kernel install --name dlnd #link the ipython interpreter to this envjupyter notebook — ip=’*’ — port=8888 — no-browser — allow-root

and you’re good to go. open in the browser the link jupyter provided.

cheers!

moshe roth

Written by

I love technology and people. I’m extremely fond of products that disrupt markets and use data to do so

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