Plans for week 47, November 17-21

Dear all, this is our second last week (our last lecture is November 24, note error in time planner, this lecture is missing there).

We hope you had a great weekend. The plans this week are:

Plans for the lecture Monday 17 November, with video suggestions etc.

Recurrent neural networks, code examples and long-short-term memory

Autoencoders (last topic this semester), second lecture, see link below for this week's lecture notes

Last lecture: November 24, note error in time planner.

Lab sessions on Tuesday and Wednesday.

Work and Discussion of project 3

Last weekly exercise with deadline November 28, available from (early morning) Tuesday November 18.

Last lab sessions: November 25 and 26

Reading rand video recommendations on RNNs

These lecture notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week47/ipynb/week47.ipynb

See also lecture notes from week 46 at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week46/ipynb/week46.ipynb. The lecture on Monday starts with a repetition on recurrent neural networks. The second lecture starts with the basics of autoenconders.

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.

RNN video at https://youtu.be/PCgrgHgy26c?feature=shared

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

Reading recommendations: Autoencoders (AE)

Goodfellow et al chapter 14, see https://www.deeplearningbook.org/contents/autoencoders.html

Rashcka et al. Their chapter 17 contains a brief introduction only.

Deep Learning Tutorial on AEs from Stanford University at http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/

Building AEs in Keras at https://blog.keras.io/building-autoencoders-in-keras.html

Introduction to AEs in TensorFlow at https://www.tensorflow.org/tutorials/generative/autoencoder

best wishes to you all,

Morten et al