GitHub code, https://github.com/jinghuazhao/tests/blob/main/AI/python/
The use of Python virtural environment is shown in MDL.sh.
GitHub, https://github.com/PacktPublishing/Generative-AI-with-Python-and-PyTorch-Second-Edition
GitHub, https://github.com/krishnonwork/mathematical-methods-in-deep-learning-ipython
The .ipynb files are largely well shown.
GitHub, https://github.com/rkneusel9/MathForDeepLearning
The sequence of function calling is illustrated by
GitHub code, https://github.com/rkneusel9/PracticalDeepLearning2E
GitHub, https://github.com/OrangeAVA/Ultimate-Neural-Network-Programming-with-Python
import model
, as composed to a package in a directory containing sub-packages and modules. A init.py file (even an empty file works) is needed.This is greatly faciliated by Google Colab, https://colab.research.google.com/.
Notably, follow “Runtime” –> “Change runtime type” to enable GPU and/or “Copy path” from the folder listing. In the case of Image_segmentation_pipeline.ipynb, it is also handy to invoke command palette via Ctrl-Shift-P and enter terminal.
This is done by posting the GitHub address to https://nbviewer.org, e.g., by replacing stable_diffusion.ipynb below with a .ipynb in the folder.
https://nbviewer.org/github/jinghuazhao/tests/blob/main/AI/python/stable_diffusion.ipynb.
cambridge-ceu fork
There are a number of changes in the forked repository, https://github.com/cambridge-ceu/Ultimate-Neural-Network-Programming-with-Python,
which have now been merged into the master branch. ↩