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Morten Hjorth-Jensen
2025-10-05 20:14:05 +02:00
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@@ -10,17 +10,21 @@ DATE: Week 41
===== Material for the lecture on Monday October 6, 2025 =====
o Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
o Building our own Feed-forward Neural Network, getting started
# * Video of lecture notes at URL:"https://youtu.be/pMRUbf9E-gM"
# * Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf"
!bblock Readings and Videos:
# * Video of lecture notes at URL:""
# * Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf"
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===== Readings and Videos: =====
!bblock
o These lecture notes
o For neural networks we recommend Goodfellow et al chapters 6 and 7.
o Rashkca et al., chapter 11, jupyter-notebook sent separately, from "GitHub":"https://github.com/rasbt/machine-learning-book"
o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
o Building Neural Networks from scratch at URL:"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
o Video on Neural Networks at URL:"https://www.youtube.com/watch?v=CqOfi41LfDw"
o Video on the back propagation algorithm at URL:"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
o We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
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@@ -28,7 +32,6 @@ We also recommend Michael Nielsen's intuitive approach to the neural networks a
!bblock Two recent books online
o "The Modern Mathematics of Deep Learning, by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen":"https://arxiv.org/abs/2105.04026", published as "Mathematical Aspects of Deep Learning, pp. 1-111. Cambridge University Press, 2022":"https://doi.org/10.1017/9781009025096.002"
o "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory, Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger":"https://doi.org/10.48550/arXiv.2310.20360"
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@@ -36,14 +39,15 @@ o "Mathematical Introduction to Deep Learning: Methods, Implementations, and The
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===== Reminder on books with hands-on material and codes =====
!bblock
* "Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch":"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
"Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch":"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
!eblock
!split
===== Lab sessions on Tuesday and Wednesday =====
o Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.
Aim: Getting started with coding neural network. The exercises this
week aim at setting up the feed-forward part of a neural network.