ECCB2022

Shorten Link: http://bit.ly/eccb2022

ECCB web: https://eccb2022.org/ntb-t03/

Slides

https://docs.google.com/presentation/d/1zAcJcWtyA-kh7AP_Hy_7OuzjQigppFaG74hLejJVlD8/edit?usp=sharing

Colab notebooks with exercises

1. Exercise 1: MNIST with fully connected network [open]

2. Exercise 2: Fine tuning CNN model to your own data [open]

3. Exercise 3: Transformers and transfer learning [open]

4. Exercise 4: Gradio demo on sequences [open] and images [open]

5. Extra exercise (not presented during the tutorial): CNN for genomic sequences - basics [open], fastai [open]

Additional materials

Where to go next:

1. Fast.ai - course, book, library

2. 🤗 - course, datasets, models, spaces, libraries

3. Genomic benchmarks - repo with genomic datasets, preprint

4. Transformers - Stanford course on transformers, DeepMind paper on algorithms

5. Transformers on genomic and proteomic sequences:

   • DNABert paper - language model trained on genome
   • AlphaFold2 paper
   • ESM2 paper - language model trained on proteome
   • ML Protein Engineering Seminar Series

6. Navigating the pitfalls of applying machine learning in genomics