Look at Float32 vs Float64: O(1) difference => O(1) derivative error.
LINK

Shadowing Methods for Forward and Adjoint Sensitivity Analysis of Chaotic Systems
+julia codes
#julia

Introduction to Machine Learning Interviews Book

https://huyenchip.com/ml-interviews-book/

https://www.aparat.com/physicsschool/live

برنامه سخنرانی ها
https://iasbs.ac.ir/seminars/phys-school/برنامه.html

Programm in pdf.

Online lecture series "Neural Data Science" on how to use #MachineLearning for #neuroscience is now complete

YouTube
GitHub

UVA DEEP LEARNING COURSE
MSc in Artificial Intelligence for the University of Amsterdam.

https://uvadlc.github.io/

GitHub

#course
#DL

scikit-learn course
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.

The course description can be found here.

GitHub
#course
#MachineLearning

Ten simple rules for structuring papers:

Overview
Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the paper’s contributions, and to integrate these contributions into a broader context. However, many scientists struggle with producing high-quality manuscripts and are typically untrained in paper writing. Focusing on how readers consume information, we present a set of ten simple rules to help you communicate the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.

[ link ]

Follow: @theTuringMachine

Great YouTube channel for data science.
https://youtu.be/TeEj3eTXdiY

Dive into Deep Learning
Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

https://d2l.ai/index.html

🎉 release swyft 0.2.0, a practical machine learning tool for robust simulation-based inference in physics.
Python package: GitHub
🪄 Simulation-based inference can do magic. How can swyft help?

Read the thread.

🍀 Suggest name of a Python package for the
"Simulation of complex network dynamics with differential equations"
using just in time compilation (jitc).
It use phase and population models for simulation of interaction nodes.
netsim and pysim already taken.

Deep Learning, which is a course on the theory and techniques of deep learning with an emphasis on neuroscience. The course runs from August 2-20.
The syllabus for this course is still in progress, here is the current draft.

[ link ]

Follow: @theTuringMachine

Machine learning, computer vision, statistics and general scientific computing for .NET

https://github.com/accord-net/framework
#ML

A Student's Guide to Python for Physical Modeling: Second Edition
#python
#book
#beginner

Still I recommend these books. 👌🤌

Jupyter Book

Jupyter Book is an open-source project for building books and documents from standard computational and data science materials such as Jupyter Notebooks and Markdown documents.


Official documentation
https://jupyterbook.org/

JITCSIM
I have written a package for high performance simulation of complex networks using just in time compilation.
The model is written in python syntax, C code is generated and run with full speed.

This is not an official release, I have made the project public to get some feedback, and know how much this could be helpful for others.

It includes the Kuramoto models and solve ODEs and SDEs.

Delay differential equations will be added soon.

Parallelising with OpenMP and Multiprocessing also supported.

To get a glance what is now available look at the notebooks.

Delay differential equations have numerous applications in science and engineering. This short, expository book offers a stimulating collection of examples of delay differential equations which are in use as models for a variety of phenomena in the life sciences, physics and technology, chemistry and economics. Avoiding mathematical proofs but offering more than one hundred illustrations, this book illustrates how bifurcation and asymptotic techniques can systematically be used to extract analytical information of physical interest.

Applied Delay Differential Equations is a friendly introduction to the fast-growing field of time-delay differential equations. Written to a multi-disciplinary audience, it sets each area of science in his historical context and then guides the reader towards questions of current interest.

#book
#DDE