pierreh.eu
Programmation & Calcul Numérique avec Python – pierreh.eu
http://pierreh.eu/formation-python
Pierre Haessig – personal website. Training "Programmation & Calcul Numérique avec Python". Seminar "Python for control". Short tuto "Python-pandas pour capteur CO2". Installing JModelica on Ubuntu 16.04. Enjeux énergétiques par le prisme d'objets du quotidien. PowerTech 2015 article online. Fake Wind Energy Conference? Roman Le Goff Latimier. Programmation & Calcul Numérique avec Python. Formation Python à destination des enseignants de classes préparatoires. Présentation de la formation. 4 séances d'1h...
courseflow.cs.illinois.edu
CS357: Fall 2014 - RELATE
https://courseflow.cs.illinois.edu/course/cs357-f14
You're not currently signed in. Sign in ». This is the fall 2014 version of CS357. If you'd like to sign up, please find the current edition of it. The version of spring 2015 is here. Numerical Methods (CS 357) Fall 2014. In rooms 1404 (lecture). And 1304 (live video). Philip N Klein, $31 Paperback, Newtonian Press, 2013. Data-Driven Modeling and Scientific Computation: Methods for Complex Systems and Big Data. J Nathan Kutz, $40 Paperback, Oxford University Press, 2013. We will be using Python. A Python...
python-prepa.github.io
6. Et pour aller plus loin — Python scientifique - ENS Paris
http://python-prepa.github.io/aller_plus_loin.html
Python scientifique - ENS Paris. 6 Et pour aller plus loin. Nous n’avons vu qu’une petite partie des possibilitiés de Python scientifique pour aller plus loin. Voici quelques pistes pour continuer à en apprendre plus! Les Scipy Lecture Notes. Un large ensemble de tutoriels. Et leurs sessions de formation, ainsi que les formations organisées par l’ afpy. Beaucoup de choses à apprendre sur les mailing-lists : numpy-discussion @. Python scientifique - ENS Paris.
nilearn.github.io
Nilearn: Machine learning for NeuroImaging in Python — Machine learning for NeuroImaging
http://nilearn.github.io/introduction.html
Machine learning for Neuro-Imaging in Python. User guide: table of contents. Please consider citing the papers. 1 Introduction: nilearn in a nutshell. 11 What is nilearn: MVPA, decoding, predictive models, functional connectivity. 111 Why is machine learning relevant to NeuroImaging? 112 Glossary: machine learning vocabulary. 13 Python for NeuroImaging, a quick start. 131 Your first steps with nilearn. 132 Scientific computing with Python. 1322 Scikit-learn: machine learning in Python. Measuring how much...
kichwacoders.com
January 2015 – Eclipse for Embedded and Scientific Tools
https://kichwacoders.com/2015/01
Eclipse for Embedded and Scientific Tools. Integrating Python For High Throughput Science. When it comes to tools-of-the-trade for scientists, Python is high on the list. This is in large part thanks to its fast, powerful libraries such as numpy and scipy. Also the dynamic and easy-to-use nature of the language lends itself well to the exploring necessary for experimental work. The accessibility of learning resources. Continue reading “Integrating Python For High Throughput Science”. January 7, 2015.
zonca.github.io
Thoughts on a career as a computational scientist | Andrea Zonca's blog
https://zonca.github.io/2014/06/career-as-a-computational-scientist.html
Thoughts on a career as a computational scientist. Recently I've been asked what are the prospects of a wannabe computational scientist, both in terms of training and in terms of job opportunities. So I am writing this blog post about my personal experience. What is a computational scientist? In my understanding, a computational scientist is a scientist with strong skills in scientific computing who most of the day is building software. Usually there are 2 main areas, in any field of science:. It is easy...
kichwacoders.com
Integrating Python For High Throughput Science – Eclipse for Embedded and Scientific Tools
https://kichwacoders.com/2015/01/07/integrating-python-for-high-throughput-science
Eclipse for Embedded and Scientific Tools. Integrating Python For High Throughput Science. When it comes to tools-of-the-trade for scientists, Python is high on the list. This is in large part thanks to its fast, powerful libraries such as numpy and scipy. Also the dynamic and easy-to-use nature of the language lends itself well to the exploring necessary for experimental work. The accessibility of learning resources. This was the challenge faced by Diamond Light Source. AnalysisRPC is a Python-to-Java b...
