آشنایی من با لیندا بر می‌گردد به هفت هشت سال قبل، راستش اولین آموخته‌ی من از دیدن فیلم‌های لیندا این بود که ببینم و بفهمم چگونه باید فیلم آموزشی ساخت!

اون موقع تازه SLR گرفته بودم، چهار پنج نفری می‌رفتیم خونه‌ی دادبه، یک یا دو اپیزود لیندا می‌دیدیم و می‌زدیم تو سر خودمون و دوربین‌هامون، و یاد می‌گرفتیم و در طول هفته به کار می‌بستیم و تجربه می‌اندوختیم. چه روزهایی بود...

همان موقع سادگی و ارزشمندی توامان آموزش‌های لیندا و میزان رشد هر هفته‌مان مرا مسحور کرده بود. فیلم‌های لیندا چند خصوصیت مهم و شاید منحصر به فرد دارند:

- بسیار تر و تمیز و شسته رفته و بی حاشیه و گزافه هستند.
- اساتید هم در مطلب و محتوا، و هم در عرضه و ارائه حقیقتا چیره دست‌اند.
- تکلیف مخاطب همان ابتدا در مورد اینکه با چه دوره‌ای روبرو است، چه پیش‌نیازی دارد، و سرآخر به کجا می‌رود، روشن می‌شود.
- تمام آموزش‌ها پروژه‌محور هستند و فایل‌های نمونه در دسترس مخاطب قرار می‌گیرند. و از آموزش‌های خشک و گنگ خبری نیست.
- تقسیم و چینه‌بندی درس‌ها کاملا حساب‌شده و در نهایت حرفه‌ای‌گری است.
- زمان هر بخش آموزشی کوتاه است و مخاطب مرحله مرحله جلو می‌رود.
- در هر آموزش اشتباهات و ابهامات رایج مطرح می‌شوند و پاسخ داده می‌شوند.
- برای هر آموزش چند سری دوره در سطوح مختلف تدارک دیده شده، و مخاطب می‌تواند از ابتدایی‌ترین سطوح آغاز کند و با توجه به نیازش تا پیشرفته‌ترین‌ها ادامه دهد.

دسترسی به همه فیلم‌های لیندا از ایران طبعا راحت نیست، چون فارغ از بحث تحریم و فیلتر پرداخت دلاری تقریبا امکان‌پزیر نیست. با گوگل‌کردن لیندا، سایت‌هایی را در داخل خواهید یافت، که فیلم‌ها را می‌فروشند! یک کارهایی هم مثلا در تهیه زیرنویس فارسی کرده‌اند.

بسیاری از دوره‌ها بخصوص عمومی‌ترها را اما می‌توانید رایگان دانلود کنید. مجموعه‌هایی که من در GIT دات آی‌آر، p30download دات آی‌آر و Yasdl دات‌کام دیدم کامل و منظم و شسته‌رفته بودند.

درک مطلب از زبان انگلیسی پیش‌نیاز استفاده از دوره‌ها است، اگر نه قطعا به زیرنویس فارسی احتیاج پیدا خواهید کرد.

@Aghmoallem

Mathematics for Machine Learning
Companion webpage to the book

https://mml-book.github.io/

pdf book

#ML

Find interesting courses in data science in this thread.

Link to twitt

Comparing two/multiple networks:
How can I measure similarity between two networks?

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging
problem of practical importance in many fields of science. Current methods of network
comparison are limited to extract only partial information or are computationally very
demanding.

Schieber et. al. introduce a method based on quantifying distance probably distribution extracted from the networks.

Quantification of network structural dissimilarities. ncomms

CompNet tool : A GUI based tool for comparison of multiple biological interaction networks.

Graph distance for complex networks. nature

And now, variational optimization of H_2^+ visualized with @matplotlib. The energy for a given contraction of the orbital is given as well as the energy for the full minimum and a few other things.

Link to twit
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 😤.

#matplotlib cheetsheet.

Non-Linear Least-Squares Minimization and Curve-Fitting for Python

#lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. It builds on and extends many of the optimization methods of scipy.optimize. Initially inspired by (and named for) extending the Levenberg-Marquardt method from scipy.optimize.leastsq, lmfit now provides a number of useful enhancements to optimization and data fitting problems.

Documentation

Neural Networks from Scratch in Python

By Harrison Kinsley
Book
https://nnfs.io/

And his YouTube series.

"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.

Github

#course
#book

Constrained optimization introduction

Full video

In this episode, @TreforBazett beautifully explains how to solve constrained optimization problems using Lagrange multipliers.

ScottPlot is a free and open-source plotting library for .NET that makes it easy to interactively display large datasets. Line plots, bar charts, pie graphs, scatter plots, and more can be created with just a few lines of code.

Have you ever wanted to quickly try some ideas popping up in your head using a Python Shell (REPL)? You might not want to open a new Jupyter Notebook to experiment with only a few lines of code.
But you might also be hesitant to use a classic Python shell since it doesn’t support auto-completion or docstring as Jupyter Notebook does. You also cannot fix the mistake in the code after hitting Enter.
What if you can turn your boring Python shell into a multi-functional shell like below?

Features:
🌱Syntax highlighting.
🌱Multiline editing (the up arrow works).
🌱Autocompletion
🌱Mouse support
🌱Support for color schemes.
🌱Support for bracketed paste
🌱Both Vi and Emacs key bindings.
🌱Support for double width (Chinese) characters.
🌱... and many other things.


Link
Open in an incognito tab!

Why you should use logging instead of print statements?

It is a common but wrong practice to add print statements inside your code to convey message about the code/function on the standard output.

Logging is a means of tracking events that happen when some software runs. The software’s developer adds logging calls to their code to indicate that certain events have occurred.

Different levels in logging:

DEBUG : Detailed information, typically of interest only when diagnosing problems.
INFO : Confirmation that things are working as expected.
WARNING : An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.
ERROR : Due to a more serious problem, the software has not been able to perform some function.
CRITICAL : A serious error, indicating that the program itself may be unable to continue running.

Logging HOWTO
Logging in Python

The Virtual Brain

TheVirtualBrain is a framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity.

The TVB EduPack on the TVB main website contains dozens of video lectures explaining TVB’s GUI and scripting interfaces. Some lectures help you reproduce the research done for a peer-reviewed neuroscience publication:
Tutorials.

Basics: My first simulation with The Virtual Brain, lecture by Paul Triebkorn
Basics: Population Models in The Virtual Brain (TVB) and the Phase Plane, lecture by Dr. Michael Schirner
Epilepsy: Modelling Epilepsy with The Virtual Brain, lecture by Julie Courtiol
Epilepsy: The Bayesian Virtual Epileptic Patient (BVEP), lecture by Meysam Hashemi
Stimulation: Brain stimulation in The Virtual Brain, lecture by Andreas Spiegler
Stimulation: Surface-based simulations and deep brain stimulations, lecture by Jil Meier
Stroke: TVB Clinical Applications - Stroke Recovery & Dementia, lecture by Randy McIntosh
Stroke: Hands-On: Modeling stroke brain, lecture by Paul Triebkorn
Model construction: Import Virtual Brain ready data into TVB and create a brain model, lecture by Patrik Bey
Model construction: Integrated workflows: Image processing pipeline, lecture by Dr. Michael Schirner
Special applications: Modeling brain dynamics in brain tumor patients using The Virtual Brain
Special applications: Simulating The Virtual Mouse Brain (TVMB), lecture by Patrik Bey
Advanced simulation: Human Brain Project (HBP) TVB-NEST co-simulation
Advanced simulation: TVB to NEST multi-scale simulation, lecture by Dionysios Perdikis
Other links are Here.

#TVB