Plans for the week of February 17-21

Dear all, welcome back to FYS5429/9429. Here are our suggestions for the lecture on Thursday. In addition, we will send later an update on all projects defined by you. This should give you all an overview of the different groups and for those of you who have not yet made up your mind, feel free to reach out to the groups.

Else, this week we plan to discuss how to implement CNNs using PyTorch, TensorFlow and own codes. After that we will start scratching the surface of Recurrent Neural Networks (RNNs). Next week we discuss in more detail the math of RNNs and how to build our own RNNs code and how to implement it using either TensorFlow or PyTorch. The lecture notes as a jupyter-notebook are at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week5/ipynb/week5.ipynb

So, this week the plans are as

1. Implementing Convolutional Neural Networks (CNNs), own codes, TensorFlow+Keras and PyTorch examples

2. Start discussion of Recurrent Neural Networks (RNNs)

3. We remind you also about the link to project examples from 2024 at https://github.com/CompPhysics/AdvancedMachineLearning/tree/main/doc/Projects/ProjectExamples

Reading recommendations

1. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications

2. For RNNs, see Goodfellow et al chapter 10.

3. Reading suggestions for implementation of CNNs in PyTorch: See Rashcka et al.'s chapter 14 and GitHub site.

4. Reading suggestions for implementation of RNNs in PyTorch: See Rashcka et al.'s chapter 15 and GitHub site.

TensorFlow examples

For TensorFlow (using Keras) implementations, we recommend (CNNs)

1. David Foster Generative Learning, see chapters 2 and 3 from the GitHub link

2. Joseph Babcock and Raghav Bali Generative AI with Python and their GitHub link, chapter 3

Best wishes to you all,

Edvin and Morten