Here is where I start to learn #Machine_Learning:
The course is available here:
Machine Learning, Andrew Ng
The whole course can be downloaded from here at once.
GitHub for Exercises in #Python.
You can check the solution in "solution" branch in case.
The course is available here:
Machine Learning, Andrew Ng
The whole course can be downloaded from here at once.
GitHub for Exercises in #Python.
You can check the solution in "solution" branch in case.
Coursera
Supervised Machine Learning: Regression and Classification
In the first course of the Machine Learning ... Enroll for free.
NetPyNE
#NetPyNE (Networks using #Python and #NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
#simulator
#NetPyNE (Networks using #Python and #NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
#simulator
Linge-Langtangen2016_Book_ProgrammingForComputations-Pyt.pdf
4.4 MB
Programming for Computations – Python
Hans Petter Langtangen
A Gentle Introduction to Numerical
Simulations with Python
Open access book
#book
#python
#basic
Hans Petter Langtangen
A Gentle Introduction to Numerical
Simulations with Python
Open access book
#book
#python
#basic
We have this awesome function called sublots_mosaic where you can pass us a layout id'ed on name
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
I read this paper a couple of days age,
https://nature.com/articles/ncomms13928
Unfortunately the attached code was in #R, So today I learned R! and provided an interface to #Python for the code.
GitHub
https://nature.com/articles/ncomms13928
Unfortunately the attached code was in #R, So today I learned R! and provided an interface to #Python for the code.
GitHub
Always use close!
I spent hours on a large code to find out I had not closed the pool in #multiprocessing in #Python
It gives you "too many files open", misleading nasty error 😤.
I spent hours on a large code to find out I had not closed the pool in #multiprocessing in #Python
It gives you "too many files open", misleading nasty error 😤.
The map() function in #Python takes a function and a series of arguments, and makes an iterable of results. It can also work on functions with multiple arguments.
(But most Python people prefer list comprehensions.)
Credit: nedbat
(But most Python people prefer list comprehensions.)
Credit: nedbat
👍1
Have you tried out the sh package for #Python yet? This package makes calling Linux and Mac terminal commands really easy! 🐍🔥
pypi.org/project/sh/
Credit: driscollis
pypi.org/project/sh/
Credit: driscollis
25_Awesome_Python_Scripts.pdf
171.4 KB
A Collection of 25 Awesome Python Scripts (mini projects)
#Python
#Python