Resources for Beginners



Papers

Reviews

Deep learning: new computational modelling techniques for genomics (paper)

Deep learning for inferring transcription factor binding sites (paper)

Decoding disease: from genomes to networks to phenotypes (paper)

Papers

Representation learning of genomic sequence motifs with convolutional neural networks (paper)

Improving representations of genomic sequence motifs in convolutional networks with exponential activations (paper)

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks (paper)

Intro to interpretability methods

https://www.twosigma.com/articles/interpretability-methods-in-machine-learning-a-brief-survey/


Python tutorials

Recommended:

  • Intro to python by Udacity (Link)

Other resources:

  • Python training (Code)

Tensorflow tutorials

Recommended:

  • Tensorflow tutorials (Link)
  • Francis Chollet’s tensorflow 2 + keras tutorial (Code)

Other resources:

  • Interpretability analysis with Tensorflow 2.0 (Link)
  • Coursera TensorFlow 2 for Deep Learning Specialization by Imperial College London (Video)

Deep Learning

DL courses

  • Convolutional Neural Networks for Visual Recognition Course at Stanford (Website, Video)

DL books

  • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (Book, Link)