Plans for the week January 22-26

Dear all, welcome back to FYS5429. This week our plans are as follows:

1. Mathematics of neural networks (NNs). The slides are not yet finished. There are some missing math. Ready evening of January 22

2. Writing own code (bring back to life your NN code). If you have written an NN code, please bring it back to life!

3. Discussion of first data set and paths for project 1

Videos on Neural Networks

• Video on Neural Networks

• Video on the back propagation algorithm

Reminder on books with hands-on material and codes

• Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch

• David Foster, Generative Deep Learning with TensorFlow

• Bali and Gavras, Generative AI with Python and TensorFlow 2

All three books have GitHub addresses from where one can download all codes. We will borrow most of the material from these three texts as well as from Goodfellow, Bengio and Courville's text Deep Learning

Reading recommendations

1. Raschkca et al., chapter 11, jupyter-notebook sent separately. See the included notes here.

2. Goodfellow et al, chapter 6 and 7 contain most of the neural network background

3.  The lecture material this week is best displayed via the jupyter-notebook at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week2/ipynb/week2.ipynb

Chapter 11 from Raschka's  text has been send as an email.

Best wishes to you all,

Keran, Morten and Ruben