Plans for the week of February 23-27

Welcome back to a new week with FYS5429/9429.

The aim of this week is to discuss recurrent neural networks (RNNs) and their basic mathematical operations. Our focus on applications will be on differential equations, however these data sets can easily be changed with those of your interest.? The lecture notes with code examples using PyTorch and Tensorflow/Keras are at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week6/ipynb/week6.ipynb

Next week we will discuss long-short-term memory RNNs, and how we can write our own code for an simple RNNs, together with the basic mathematics needed. After that we will start discussing Autoencoders and Transformers, before we dive into generative models.

Else, at the lab we will discuss the various projects. The lecture is in-person but if you cannot attend in person, you can either join via zoom or watch the recording afterwards.

Reading recommendations

For RNNs, see Goodfellow et al chapter 10, see?https://www.deeplearningbook.org/contents/rnn.html.

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

Best wishes to you all,

Morten, Oda and Ruben