tests

Command history

This is with ~/.python_history for interactive Python sessions.

Miscellaneous AI-related materials in Python

GitHub code, https://github.com/jinghuazhao/tests/blob/main/AI/python/

Local setup

The use of Python virtural environment is shown in MDL.sh.

AI and ML for Coders in PyTorch

GitHub, https://github.com/lmoroney/PyTorch-Book-FIles

Deep Learning Crash Course

GitHub, https://github.com/DeepTrackAI/DeepLearningCrashCourse

Generative AI with Python and PyTorch, 2e

GitHub, https://github.com/PacktPublishing/Generative-AI-with-Python-and-PyTorch-Second-Edition

Math and Architectures of Deep Learning

GitHub, https://github.com/krishnonwork/mathematical-methods-in-deep-learning-ipython

The .ipynb files are largely well shown.

Math for Deep Learning

GitHub, https://github.com/rkneusel9/MathForDeepLearning

The sequence of function calling is illustrated by

• Chapter 10, mnist.py <- NN.py.

Practical Deep Learning, 2e

GitHub, https://github.com/rkneusel9/PracticalDeepLearning2E

Ultimate Neural Network Programming with Python

GitHub, https://github.com/OrangeAVA/Ultimate-Neural-Network-Programming-with-Python

• Chapter 5, clustering.py, pca.py (after adding empty lines), SVM_with_visualization.py are ready to run. However, tsne.py is now tsne..ipynb upon use of train.py to download the data.
• Chapter 9, we have mlp.ipynb and miso_nn_tensorflow.ipynb.
• Chapter 10, we have ft.ipynb, Image_segmentation_pipeline.ipynb¹. The latter does function calls in main.py from each .py file in a folder to be imported as a module, e.g., 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.

.ipynb

Code extraction

This is furnished in two steps as follows,

# --> markdown
source rds/software/py3.11/bin/activate
jupyter nbconvert --to markdown Chapter8.ipynb
# --> Python
module load ceuadmin/node/
npm install -g codedown
which codedown
cat Chapter8.md | codedown python > Chapter8.py

Visualisation

Visual Studio code generally does a very good job but it 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.

One can also post the GitHub address to https://nbviewer.org, e.g., by replacing stable_diffusion.ipynb below with a .ipynb in the folder, e.g., https://nbviewer.org/github/jinghuazhao/tests/blob/main/AI/python/stable_diffusion.ipynb.

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1. cambridge-ceu fork

   There are a number of changes in the forked repository, https://github.com/cambridge-ceu/Ultimate-Neural-Network-Programming-with-Python,

   • model/att_net.py is actually model/att_unet.py
   • changes of file paths
   • rename Chatper –> Chapter for 6-8, 10.

   which have now been merged into the master branch. ↩