Quantitative #Economics with
#Python

Link

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.

After the Andrew Ng course I think this book is a good resource to learn #Tensorflow:
Machine Learning Using TensorFlow Cookbook, 2021

This website provides one of the most lightweight introductions to #machine_learning I have seen.

If I were to start learning #ML all over again, the structure and concepts covered in this resource would provide a good start.

got from Omarsar0

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

A basic intro to stats for neuroscientists and all course materials are open here
Jupyter notebook slides & RISE with code to play around with to build an intuition for stats...

#neuroscience
#course

Programming for Computations – Python
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

Computational Statistics in Python

This is great.👌

NMA-Computational Neuroscience: July 5-23, 2021
Content: https://github.com/NeuromatchAcademy/course-content
• NMA-Deep Learning: Aug 2-20, 2021
Content: https://github.com/NeuromatchAcademy/course-content-dl

Applications for interactive students and teaching assistants (paid positions) are due May 7th 2021. Application Portal: https://portal.neuromatchacademy.org/

#Neuromatch

Machine learning in Python with scikit-learn

Ref. 41026
Duration: 8 weeks
Effort: 35 hours
Pace: ~4h15/week

Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!

#ML
#scikit_learn
#course

Visualizing the similarity of two networks


For issue of blank cell on chrome:
install chrome extension