#matplotlib

Introduction of #tensorflow with Python
▻https://hackernoon.com/introduction-of-tensorflow-with-python-f4a9624f2ab2?source=rss----3a8144

https://cdn-images-1.medium.com/max/1024/1*JGDhEOqWVmzqofJ7xSS0KA.jpeg

Photo on UnsplashMachine learning is the most popular part of our technology world. TensorFlow is also a popular open source and it is a framework of deep learning. The Deep Learning is part of Machine Learning.Photo on UnsplashDeep learning is also a large part of machine learning methods based on learning data presentations — “as opposed to task-specific algorithms.”Photo on @GoogleWhat is TensorFlow?It’s a framework to perform computation very efficiently, and it can tap into the GPU (Graphics Processor Unit) in order too speed it up even further. This will make a huge effect as we shall see shortly. TensorFlow can be controlled by a simple Python API, which we will be using in this Article.Graphs and TensorsWhen a native computation is done in many programming languages, it is usually (...)

#matplotlib #python3 #machine-learning #deep-learning

Je donne un cours de traitement numérique du signal, et je veux me dématlabiser, par conviction libriste. Apparemment on peut utiliser R pour le traitement de signal

▻http://blog.revolutionanalytics.com/2013/10/r-and-signal-processing.html

Quelqu’un a essayé ? Des idées de tutoriaux, de librairies, des conseils ? Ou des meilleures options libres alternatives ? #julia ? #octave ? #scilab ? #python

#matlab #R #signal_processing #logiciel_libre

Data Science Toolbox
▻http://datasciencetoolbox.org

The Data Science Toolbox is a virtual environment based on Ubuntu Linux that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run the Data Science Toolbox either locally (using #virtualbox and Vagrant) or in the cloud (using Amazon Web Services).

We aim to offer a virtual environment that contains the software that is most commonly used for data science while keeping it as lean as possible. After a fresh install, the Data Science Toolbox contains the following software:

Python, with the following packages: IPython Notebook, #NumPy, SciPy, #matplotlib, pandas, scikit-learn, and SymPy.

#R, with the following packages: #ggplot2, plyr, dplyr, lubridate, zoo, forecast, and sqldf.

dst, a command-line tool for installing (...)

Install Scientific Python on Mac OS X | Pen and Pants
▻http://penandpants.com/2013/04/04/install-scientific-python-on-mac-os-x

sur 10.9.4 j’ai suivi cette doc ; seule modif : gfortran est désormais dans gcc

#scipy #numpy #pandas #openblas #matplotlib #ipython #python

Grr, j’ai passé des heures hier à essayer de représenter des matrices 2D sous forme d’images avec #numpy et #matplotlib.
Genre :

import numpy as np
import matplotlib.pyplot as plt

im = np.ones((32,32))
plt.imshow(im, cmap=plt.get_cmap(’gray’))
plt.show()

produit une image noire ... alors que je m’attends à du blanc ...
–> c’est à cause de l’autoscaling du niveau de gris

import numpy as np
import matplotlib.pyplot as plt

im = np.ones((32,32))
im[0][0] = 0
plt.imshow(im, cmap=plt.get_cmap(’gray’), vmin = 0, vmax = 1)
plt.show()

C’est mieux mais ça fait une interpolation alors que je veux voir les valeurs par élément de la matrice. Et tant qu’à faire, afficher les coordonnées depuis le coin inférieur gauche.

import numpy as np
import matplotlib.pyplot as plt

im = np.ones((32,32))
im[0][0] = 0
plt.imshow(im, cmap=plt.get_cmap(’gray’), vmin = 0, vmax = 1, interpolation = ’none’, origin = ’lower’)
plt.show()

Pfou ...