Plans for week 42, October 14-18

Dear all, we hope your weekend turned out the best possible way.  Here are our plans for the coming week:

Lecture October 14, 2024

1. Building our own Feed-forward Neural Network and discussion of project 2

Readings and videos.

1. These lecture notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week42/ipynb/week42.ipynb

2. For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7.

3. Neural Networks demystified at https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs

4. Building Neural Networks from scratch at https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex

5. Video on Neural Networks at https://www.youtube.com/watch?v=CqOfi41LfDw

6. Video on the back propagation algorithm at https://www.youtube.com/watch?v=Ilg3gGewQ5U

I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at http://neuralnetworksanddeeplearning.com/chap4.html.

Material for the active learning sessions on Tuesday and Wednesday

• Exercise on starting to write a code for neural networks, feed forward part. We will also continue ur discussions of gradient descent methods from last week. If you have time, start considering the back-propagation part as well (exercises for next week).  The exercise set for this week are at https://github.com/CompPhysics/MachineLearning/blob/master/doc/LectureNotes/exercisesweek42.ipynb

• Discussion of project 2

Note: some of the codes will also be discussed next week in connection with the solution of differential equations.

Best wishes to you all,

Fahimeh, Ida, Karl Henrik, Mia, Morten, Odin and Sigurd