Morten Hjorth-Jensen
34 lines
1.8 KiB
Plaintext
34 lines
1.8 KiB
Plaintext
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!split
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===== Plan for week 41 =====
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!bblock Material for the active learning sessions on Tuesday and Wednesday
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* Exercise on writing your own stochastic gradient and gradient descent codes. This exercise continues next week with studies of automatic differentiation
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* One lecture at the beginning of each session on the material from weeks 39 and 40 and how to write your own gradient descent code
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* Discussion of project 2
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* Your task before the sessions: revisit the material from weeks 39 and 40 and in particular the material from week 40 on stochastic gradient descent
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!eblock
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!bblock Material for the lecture on Thursday October 12, 2023
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* Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
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* Building our own Feed-forward Neural Network
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* "Video of lecture notes":"https://youtu.be/5-RRTO9uDvI"
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* "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct12.pdf"
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* Readings and Videos:
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* These lecture notes
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* For neural networks we recommend Goodfellow et al chapter 6.
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* "Neural Networks demystified":"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
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* "Building Neural Networks from scratch":"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
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* "Video on Neural Networks":"https://www.youtube.com/watch?v=CqOfi41LfDw"
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* "Video on the back propagation algorithm":"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
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I 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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!eblock
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!split
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===== Lecture Thursday October 12 =====
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