Scientific Programming
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Tutorials and applications from scientific programming

https://github.com/Ziaeemehr
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Understanding Deep Learning
by Simon J.D. Prince
To be published by MIT Press.

Draft
GitHub
dimreductiontechniquespydatadcoct2016.pdf
1.2 MB
A Practical Guide to Dimensionality Reduction Techniques
Multimodal Deep Learning
Free Ebook
If you start reading Fastbook
To dowload image from bing search engine, it force you to have an api key, here is a solution to use DuckDuckGo search engine, without api key:
Deep Learning with PyTorch Step-by-Step

Notebooks

use β€œOpen in Colab” extention in your browser to execute online.
decorators.pdf
55.8 KB
Python Decorator
here are some examples I used for training.

@scientific_programming
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A unique masterpiece book which combines information theory and machine learning:

"Information Theory, Inference, and Learning Algorithms" by Sir MacKay
πŸ†“ PDF
with 16 video lectures πŸŽ₯:
YouTube
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Internally it uses a U-Net based foreground/background semantic segmentation and yields the post processed results. πŸ‘πŸ»

So, if you try it, library would download the trained model first. (takes a bit of time)
Credit: Akshay
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How to extract attached subtitle from movie and translate it to any target language?

use command:

ffmpeg -hide_banner -i Movie.mkv -map 0:s:0 subs.srt

to extract the attached subtitle.
then upload one or bunch of subtitles to

https://www.syedgakbar.com/projects/dst

and select target language. Finally store translated str files.
Done!
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"Information Theory: From Coding to Learning"

coming out soon: 600+ pages with 150+ exercises
by Y. Polyanskiy and Y. Wu
Cambridge University Press

https://people.lids.mit.edu/yp/homepage/papers.html
book draft
Access to chatgpt4 (for free) with internet access, which produce no fake citation through
www.perplexity.ai

Here I asked what do you know about modeling Alzheimer disease?
Linkes inside the answer works correctly.

Have fun using AI. πŸ‘
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High-Performance Computing with Python

The course covers the following topics:
- Vectorization with NumPy and the SciPy stack
- Profiling python code
- Extending python with cython, cffi and f2py
- Just-in-time compilation with numba
- Distributed-memory parallel programming with python
- Interfaces to GPUs

GitHub

Video 1
Video 2
Video 3