courses
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI) (by SkalskiP)
DNABERT
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome (by jerryji1993)
| courses | DNABERT | |
|---|---|---|
| 7 | 1 | |
| 4,556 | 543 | |
| - | - | |
| 5.4 | 3.1 | |
| 15 days ago | about 2 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
courses
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If you are looking for free courses about AI, LLMs, CV, or NLP, I created the repository with links to resources that I found super high quality and helpful. The link is in the comment.
I found it: https://github.com/SkalskiP/courses
https://github.com/SkalskiP/courses/discussions/20. Wouldhttps://github.com/SkalskiP/courses/discussions/20 that format be helpful?
- Repo con cursos gratis sobre IA (en inglés)
- GitHub - SkalskiP/courses: This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
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Anyone know of any good video lectures for Computer Vision? From great professors at well-regarded universities
This is a popular repo with cv specific resources https://github.com/SkalskiP/courses
- Curated collection of high quality ai resources
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If you are looking for courses about Artificial Intelligence, I created the repository with links to resources that I found super high quality and helpful. The link is in the comment.
link: https://github.com/SkalskiP/courses
DNABERT
Posts with mentions or reviews of DNABERT.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[D] New to DNABERT
If I want to get started, they said it's optional to pre-train (so you can skip to step 3). This is where I got tripped up: "Note that the sequences are in kmer format, so you will need to convert your sequences into that." From what I understand, you need to do this so that all of the sequences are the same length? So kmer=6 means all of the sequences are length 6? Someone suggested that I take the first nucleotide in the promoter and grab 3 nucleotides before and 3 nucleotides after (+/-3 bases). I don't think that's how the kmer thing works though? I tried replicating how I think it works down below (I got confused on the last row of the 'after' df). Please correct me if I'm wrong!
What are some alternatives?
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stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
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