Step 1, get a high-level understanding
- Watch statquest by Josh Starmer.
- 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
More papers I collected can be found at https://github.com/crazyhottommy/Machine_learning_drug_discovery
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!
Large language models
Other learning resources
https://www.deeplearning.ai/short-courses/ by Andrew Ng
Practical deep learning https://course.fast.ai/
Graph Neural Net works a blog post by Matt B.
More papers and resources that I curated https://github.com/crazyhottommy/machine-learning-resource
- 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 https://divingintogeneticsandgenomics.ck.page/6steps