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.