My 4-steps to learn deep learning for genomics

Step 1, get a high-level understanding

  • 1blue3brown deep learning playlist

Step2, code it out!

If you are into python, watch “The spelled-out intro to neural networks and backpropagation: building micrograd”:

I still code in R for most of the time, so I walk through the R code in the deep learning with R book. It is pretty accessible. I then go through the examples and write blog posts on it.

See my posts:

Step 3, apply it to real biological examples

The examples in the books are usually not biological data, either it is movie review or MNIST image classification etc. To understand how deep learning is applied to biological data, read


Blog posts:

More papers I collected can be found at

I then apply it to a biological problem using biological datasets.

See my blog posts:

Step 4, apply the learning to a real project

At immunitas, we are really interested in the TCR-seq data. Matthew Bernstein and I are working on a deep learning project. More to come!

Other learning resources


  • Deep learning can be daunting if you just started. For me, it is good enough to understand at a high level, what hyperparameters can be tuned, understand when to use which model, and aware of its strength and limitations.

Read my blog post: Has AI changed the course of Drug Development?

  • Learn meta-learning skills: the skills of how to learn new things. With determination and a framework, you can learn anything!

Watch my youtube video: 6-step framework to learn computational biology

and get a free PDF with all my curated links to start learning computational biology! From Zero to Hero


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