Deep learning (M2 Data Science - Polytechnique / Paris-Saclay)

Numerical analysis (ENSAE 1A)

  • Python notebooks with (some) corrected exercices are available in this repository – either use git to check it out or download it as a zip file.
  • I highly recommend looking at the Python notebooks from Numerical Tours of Data Sciences for those of you who want to dig further into optimization/data science/graphics.
  • Scipy Lecture Notes is an excellent resource for improving your skills in numpy and matplotlib, among other useful libraries for numerical analysis and data science.
  • If you want to work from your laptop, I recommend that you install Anaconda for your OS.
  • If you do not like the spyder IDE, you should try directly working from Jupyter notebooks: you may prefer this workflow.

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