TK • 木下
@wordsofteekay
coding . ml . math . writing . books . https://github.com/imteekay
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Just finished writing the blog post! ✅ Let's keep learning!
[ML Grind] > Working on PyTorch skills: did some computer vision stuff > Writing: in the middle of the new blogpost about mastering PyTorch > Applying what I've learned about PyTorch in a new healthcare project: CV for healthcare data > Made small progress on mathacademy and the…
![wordsofteekay's tweet image. [ML Grind]
> Working on PyTorch skills: did some computer vision stuff
> Writing: in the middle of the new blogpost about mastering PyTorch
> Applying what I've learned about PyTorch in a new healthcare project: CV for healthcare data
> Made small progress on mathacademy and the…](https://pbs.twimg.com/media/G6e3vbXXkAAy9yB.jpg)
From time to time, I stumble upon this video by Dr. Gabor Maté, which talks about working excessively to the limit of ignoring what truly matters. Not sure if everybody feels the same way, but the message I got from this short was that it's not really about working hard or not,…
[ML Grind] My main focus these days is mastering pytorch and making progress on the ml for bio projects. Here is the breakdown of things I've been doing to achieve those two: > Building simple projects from scratch in pytorch > Rebuilding my healthcare project from keras to…
![wordsofteekay's tweet image. [ML Grind]
My main focus these days is mastering pytorch and making progress on the ml for bio projects. Here is the breakdown of things I've been doing to achieve those two:
> Building simple projects from scratch in pytorch
> Rebuilding my healthcare project from keras to…](https://pbs.twimg.com/media/G5oinzfWkAA0b_B.jpg)
[ML grind] > Making progress in deep learning for biology, and learning more problems in the field (DNA-TF binding, Drug-drug interaction) > First time trying an architecture that includes both convolutional layers (CNN) and transformers (self-attention) to learn local and…
![wordsofteekay's tweet image. [ML grind]
> Making progress in deep learning for biology, and learning more problems in the field (DNA-TF binding, Drug-drug interaction)
> First time trying an architecture that includes both convolutional layers (CNN) and transformers (self-attention) to learn local and…](https://pbs.twimg.com/media/G5VP-9RXQAAvkT8.jpg)
[ML Grind] Got a cup of coffee, lofi, and started studying. I had this morning: > Finding patterns in DNA sequences (motif) for TF-DNA binding > Starting with CNN and then moving on to transformers to train the model to make predictions and work on this bio problem > On the…
![wordsofteekay's tweet image. [ML Grind]
Got a cup of coffee, lofi, and started studying. I had this morning:
> Finding patterns in DNA sequences (motif) for TF-DNA binding
> Starting with CNN and then moving on to transformers to train the model to make predictions and work on this bio problem
> On the…](https://pbs.twimg.com/media/G5Jpt64XEAAQFxU.jpg)
Very insightful blog post about how to use a top-down approach to learning, and understand the 'whole game' even before learning all the details, especially in ML, where the field is very empirical and requires a whole lot of experimentation and coding. medium.com/@init_27/how-n…
Friday night of studies! Continue working on my transformers studies, biomedicine, molecular biology, and keep making progress to solve interesting and impactful problems.
I published this blogpost on linked*n and it turns out, there's a whole community of biology/healthcare/ml people there. There are interesting people everywhere. It was surprising for me to get connected with more people there than here, tbh.
📝 Deep Learning for Biology: Predicting Protein Functions from Sequences I've been reading the book DL for bio and it's the piece I was missing to understand how I can use ML for these kinds of problems. Or, in other words, reframe biology problems as data problems. Together…
This type of question is asked from time to time, so I wanted to take a few minutes to write about it. First, this type of question is a great reminder for me to ask why I'm doing what I'm doing. It's a great tool to reflect. It all comes back to when I read the 'The Art of…
hey man I'm just curious, where does your interests in ML for biology comes from? is it new? I'll like to learn ML for Bio too (: I always thought that ML for Bio was very niche or 'new' but looking at your resources it's seems a huge field, I'll take a deeper look when I can
📝 Deep Learning for Biology: Predicting Protein Functions from Sequences I've been reading the book DL for bio and it's the piece I was missing to understand how I can use ML for these kinds of problems. Or, in other words, reframe biology problems as data problems. Together…
My team bought this book and we created a weekly bookclub to discuss each chapter. Still on the first chapters, but looking good. My team is a performance engineering team, so we are a bit biased.
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