Plans for the week of February 24-28

Dear all, welcome back to FYS5429/9429. We hope the week has started the best possible way for you all.

This week we start with recurrent neural networks, with a basic overview and their math. Note that the jupyter-notebook for this week, will be updated with more material (basic math of RNNs) the next two days. 

We will continue with RNNs next week as well, and discuss how we can encode them, plus more on their math.

For those of you interested in say large language models, RNNs served as prototypes and could represent a full semester project, like the CNN coding project. 

Plans for the week February 24-28, 2025

Intro to and mathematics of Recurrent Neural Networks (RNNs)

More material will be added (Tuesday and Wednesday this week).

Reading recommendations

For RNNs, see Goodfellow et al chapter 10, see https://www.deeplearningbook.org/contents/rnn.html. This book gives a nice overview.

Reading suggestions for implementation of RNNs in PyTorch: see Rashcka et al.'s chapter 15 and GitHub site at https://github.com/rasbt/machine-learning-book/tree/main/ch15.

TensorFlow examples

For TensorFlow (using Keras) implementations, we recommend

David Foster, Generative Deep Learning with TensorFlow, see chapter 5 at https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch05.html

Joseph Babcock and Raghav Bali Generative AI with Python and their GitHub link, chapters 2 and 3 at https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2. The material here is not optimal however. The book is better on generative methods.

The jupyter-notebook for this week is at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week6/ipynb/week6.ipynb

Note that more material will be added.

A complete list of all project groups will be send later today to you all. We believe we have been able to catch most of you.

Cheers and all the best,

Edvin and Morten