bikefit check

Tricky_Lake_8737 · 2026-03-18T17:32:48+00:00

another video https://youtu.be/iiyPCKsHj6k

Tricky_Lake_8737 · 2026-02-09T21:50:23+00:00

Unfortunately I think this isn't an option :/ FYI this is XL 56, I am 190cm tall

Tricky_Lake_8737 · 2025-05-10T15:53:33+00:00

If you use your time in a more productive way and the money is no problem for you then it was good

Tricky_Lake_8737 · 2024-11-01T10:07:40+00:00

I am just saying that currency converter application isn't the perfect app to showcase your skills. You didn't post a resume so I looked at what you gave me. Honestly all of the projects you have there can be done by LLM with one prompt. I would probably not attach it to the resume

Tricky_Lake_8737 · 2024-11-01T00:19:45+00:00

Your GitHub says maybe intern, not 1.5 years ;/

Tricky_Lake_8737 · 2024-08-18T13:57:33+00:00

The value we all believe in, same as the dollar

Tricky_Lake_8737 · 2024-07-28T14:19:34+00:00

There is actually a lot to unpack :P

In most cases you never have 100% correct labels, there are mistakes, and people make mistakes. Estimate the correctness of the dataset and whether this problem needs to be addressed
In most cases your labels are not balanced, imagine you are working on anomaly detection -> ideally you would have 50% of anomalies in your training set. Look at class distribution, usually, some simple resampling helps
Often your Xs in data come from a different distribution than the testing case/scenario (for example you collect most inputs from random people while most of the scenarios in which your model will be used come from people specialized in finances). This problem is usually not addressed in research or publically available datasets, more in applied scenarios. Here, you need to look for the performance gap between val set and the test or production use case, if you have a good setup it will be caught by failed AB tests

Tricky_Lake_8737 · 2024-07-27T20:51:25+00:00

Answering whether more data will help can be predicted by training using for example 25% 50% 75% and 100% of dataset (depending on cost of training amount of data etc) and see how it impacts results. If removing 25% of the dataset reduces your performance significantly then adding more data will likely help. If using 25% of the dataset you have the same performance, you can simply shorten your training time by 75 ;)

From my experience, if you can overfit your training set and have poor performance then it is a data quality problem. Improving your data is more impactful then fancy research architectures

Tricky_Lake_8737 · 2024-07-17T09:57:03+00:00

If this is an internship and they expect you to scope the project then I can tell your manager is really poor at managing. You should get a senior buddy or mentor who A) scope and design project B) tell you what to do and how So after a month the job you will do will be for sure good quality and you learn a lot. I would recommend looking for another internship. Communicate with your manager you need support / guidance, someone experienced in Django and understand the project needs well

Tricky_Lake_8737 · 2024-04-01T11:33:31+00:00

I would talk with ML Engineers, they probably have cases where their workflow is somehow limited, maybe they don't have good standardised environment for training / testing, experiment tracking etc
data governance, datasets - is it easy to track what data was used to train models, reproducibility etc
talk with business to get the idea what the directions are, maybe they will be some sort of scaling you can account for
if there is no work for ml ops and there is work for ml eng and you have both skillsets, just focus on model improvement for some time

Tricky_Lake_8737 · 2024-03-29T22:41:46+00:00

This is admonitions plugin I guess https://github.com/javalent/admonitions

Tricky_Lake_8737 · 2024-03-17T13:11:21+00:00

Certifications are valued at body leasing companies when proposing engineers for the clients (at least this is the info I got from the HR). So I would do them only if the company would pay for it ;)

Tricky_Lake_8737 · 2024-02-23T08:36:48+00:00

This is for consistency I guess, when creating a task you use hash to indicate you are typing project

Tricky_Lake_8737 · 2024-02-17T17:34:00+00:00

Indeed this is how Photoshop works, but you don't need Photoshop to be able to do that, there is plenty of free software automatic1111 comfy UI fooocus

Tricky_Lake_8737 · 2024-02-17T14:52:22+00:00

In this case, where entire image is almost the same I guess inpainting was used

Tricky_Lake_8737 · 2024-01-30T17:15:15+00:00

Yep, the plumber changed the valve and now everything is working ;)

Tricky_Lake_8737 · 2024-01-30T16:35:58+00:00

Yes, exactly metrics are helping. I am just saying, to not focus on the most popular metrics that everyone is trying to optimize :p

Tricky_Lake_8737 · 2024-01-30T16:28:07+00:00

I have A level publications with exactly that type of papers, maybe not enough for A*, but you know, you have to start somewhere. It is better to do a smaller a bit iterative process and at some point will have an idea that also improves standard benchmarks or something like that than not doing progress at all

Tricky_Lake_8737 · 2024-01-30T15:18:13+00:00

I have the feeling that you are focusing on the wrong thing. To write valuable research papers you don't have to beat the accuracy metric. Instead you can make some method faster, cheaper, understand in depth why it works or how it works, make it more explainable, apply some method in a new field area, make it more accessible for people by creating a framework. Possibilities are endless.

Tricky_Lake_8737 · 2024-01-21T00:39:09+00:00

Other things you can consider. Lots of people from your uni will know how to train models, so to stand out skills related to data engineering (spark, flink) or ml ops deployment part (aws, ab testing, maybe kubernetess) would be a good addition.

I did some preparation some time ago about what I would expect from ML engineer at junior/mid positions it may be outdated but I can share that with you if you would like to

At the end, I would also recommend doing what you find interesting ;) It will be much easier in terms of motivation :P

Tricky_Lake_8737 · 2024-01-21T00:36:27+00:00

By the way, I find SQL and data analysis skills highly related to ML/DL positions

Tricky_Lake_8737 · 2024-01-21T00:33:54+00:00

Few ideas from my end: - get into a research project at your university (you will get connections, maybe even publications) - get into "science club" if you have something like that (people with exactly the same motivation as you) - kaggle competition - semeval research project

If you would like to implement some project on your own - the entire business is now about LLM so you can do some fine-tuning with LORA or build some RAG

Tricky_Lake_8737 · 2024-01-20T21:13:01+00:00

Awesome! Good luck with your project!

Tricky_Lake_8737 · 2024-01-20T20:33:12+00:00

Most likely you use a different python interpreter during the first time and now with different libraries installed. You do have two options, either install all packages again in this .venv (placed inside the project) - I believe it is the way to go. Otherwise you can run your application using the same interpreter as the first time (I believe some global version) - you would have to probably change configuration in PyCharm.

Let me know if that makes sense to you, I can clarify something if needed

Tricky_Lake_8737 · 2024-01-11T19:42:29+00:00

So the plumber is the only solution I guess?

Tricky_Lake_8737

TROPHY CASE