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ep:labs:05 [2020/11/10 16:32]
ioan_adrian.cosma [Python Scientific Computing]
ep:labs:05 [2020/11/10 16:44] (current)
ioan_adrian.cosma [Python Scientific Computing Resources]
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-===== Contents ===== 
  
-{{page>:​ep:​labs:​05:​meta:​nav&​nofooter&​noeditbutton}} 
  
  
-===== Python Scientific Computing =====+===== Python Scientific Computing ​Resources ​=====
  
 In this lab, we will study a new library in python that offers fast, memory efficient manipulation of vectors, matrices and tensors: **numpy**. We will also study basic plotting of data using the most popular data visualization libraries in the python ecosystem: **matplotlib**. ​ In this lab, we will study a new library in python that offers fast, memory efficient manipulation of vectors, matrices and tensors: **numpy**. We will also study basic plotting of data using the most popular data visualization libraries in the python ecosystem: **matplotlib**. ​
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 Python is very easy to use, but the downside is that it's not fast at numerical computing. Luckily, we have very eficient libraries for all our use-cases. Python is very easy to use, but the downside is that it's not fast at numerical computing. Luckily, we have very eficient libraries for all our use-cases.
  
-## Libraries+**Core computing libraries**
  
-### Core computing ​libraries+  * numpy and scipy: scientific ​computing 
 +  * matplotlib: plotting library
  
-- `numpy` and `scipy`: scientific computing +**Machine Learning**
-- `matplotlib`:​ plotting library+
  
-### Machine Learning +  * sklearn: machine learning toolkit 
-- `sklearn`: machine learning toolkit +  ​* ​tensorflow: deep learning framework developed by google 
-- `tensorflow`: deep learning framework developed by google +  ​* ​keras: deep learning framework on top of `tensorflow` for easier implementation 
-- `keras`: deep learning framework on top of `tensorflow` for easier implementation +  ​* ​pytorch: deep learning framework developed by facebook
-- `pytorch`: deep learning framework developed by facebook+
  
  
-## Statistics and data analysis +**Statistics and data analysis** 
-- `pandas`: very popular data analysis library + 
-- `statsmodels`: statistics+  * pandas: very popular data analysis library 
 +  ​* ​statsmodels:​ statistics
  
 We also have advanced interactive environments:​ We also have advanced interactive environments:​
-- Ipython: advanced python console + 
-Jupyter: notebooks in the browser+  * IPython: advanced python console 
 +  ​* ​Jupyter: notebooks in the browser
  
 There are many more scientific libraries available. There are many more scientific libraries available.
-===== Tutorial ===== 
  
  
-{{namespace>​:ep:labs:05:contents:tutorial&nofooter&​noeditbutton}}+Check out these cheetsheets for fast reference to the common libraries: 
 + 
 +**Cheat sheets:** 
 + 
 +  - [[https://​perso.limsi.fr/​pointal/​_media/​python:cours:mementopython3-english.pdf)|python]] 
 +  - [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Numpy_Python_Cheat_Sheet.pdf|numpy]] 
 +  - [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Python_Matplotlib_Cheat_Sheet.pdf|matplotlib]] 
 +  - [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Scikit_Learn_Cheat_Sheet_Python.pdf|sklearn]] 
 +  - [[https://​github.com/​pandas-dev/​pandas/​blob/​master/​doc/​cheatsheet/​Pandas_Cheat_Sheet.pdf|pandas]] 
 + 
 +**Other:​** 
 + 
 +  - [[https://​stanford.edu/​~shervine/​teaching/​cs-229/​refresher-probabilities-statistics|Probabilities ​Stats Refresher]] 
 +  - [[https://​stanford.edu/​~shervine/​teaching/​cs-229/​refresher-algebra-calculus|Algebra]] 
  
 +<​note>​This lab is organized in a Jupyer Notebook hosted on Google Colab. You will find there some intuitions and applications for numpy and matplotlib. Check out the Tasks section below.</​note>​
  
 ===== Tasks ===== ===== Tasks =====
ep/labs/05.txt · Last modified: 2020/11/10 16:44 by ioan_adrian.cosma
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