Machine Learning with Julia
Contact
Prof. Dr. Claus Möbus
Room: A02 2-226
claus.moebus@uol.de
-------------------------------------------
Secretary
Manuela Wüstefeld
Room: A02 2-228
Tel: +49 441 / 798-4520
manuela.wuestefeld@uol.de
-------------------------------------------
Machine Learning with Julia
Machine Learning with Julia/Pluto.jl
Learning by de- and reconstruction – this is my motto when reading the book Understanding Deep Learning by Simon J.D. Prince, MIT Press, 2024. In 2012 he published a book titled Computer Vision: Models, Learning, and Inference. While that was based on Bayesian methodology the new book shifts the focus on a nonBayesian approach. I expect that further books of the same or other autors will combine both approaches and deepen the machine learning approach towards understanding and creating.
Learning by de- and reconstruction means that I take Prince’s Python notebooks, analyze and deconstruct the content and reconstruct the meaning in Julia/Pluto notebooks leaning on libraries such as FLUX.jl and LUX.jl.
-
Julia/Pluto-UDL-Notebook 1.1 -- Background Mathematics
- Supervised Learning
- Shallow Neural Networks
- Shallow Neural Networks I
- Shallow Neural Networks II
- Shallow Network Regions: Julia/Pluto-UDL-Notebook 3.3
- Activation functions
-----------------------------------------------------------------------------------------
This is all draft for personal use; comments, bug reports, or proposals are welcome:
claus.moebus(at)uol.de
-----------------------------------------------------------------------------------------