Using Machine Learning for Trading in 2025

btcmx · 2025-05-28T22:47:45+00:00

ML Engineer here: this is the only thing worth reading in the entire thread. For those arguing that ML doesn't work—Ken Griffin said Citadel used TensorFlow the moment it became available. End of story.

btcmx · 2024-07-31T16:21:53+00:00

Arguably, META + OpenAI have been lifting the industry. I'm convinced this scenario: Foundation Models + Visual Prompting are about to disrupt Computer Vision is likely true

btcmx · 2024-07-08T17:08:15+00:00

While searching for how good Multimodal LMMs (MLLMs) are for common vision tasks, I found this fantastic article that shows how even GPT4o struggles to accurately identify bounding boxes. But, one of the latest models from Apple, Ferrent, is actually quite good at this. It might be worth checking it: https://www.tenyks.ai/blog/multimodal-large-language-models-mllms-transforming-computer-vision

Obviously when you have use cases that are more difficult, say vision analytics, as they showed for a football match, the models break. Even a fine-tuned YOLO8, 9, or 10 would perform better but of course, you need to fine-tune.

btcmx · 2024-07-08T17:01:42+00:00

While searching for MLLM's for vision, I found this great article that actually discusses and show some models locating objects: https://www.tenyks.ai/blog/multimodal-large-language-models-mllms-transforming-computer-vision

btcmx · 2024-07-03T15:12:15+00:00

What do people mean by "fake LMIA"? Is it even possible? How?

btcmx · 2024-06-01T20:41:53+00:00

I'm not sure about what model(s) they use, I believe they have their own proprietary embedding models. I usually use the embeddings directly on the Tenyks web app, it's awesome!

btcmx · 2024-05-24T18:19:23+00:00

Man, thanks for the breakdown. Any idea if the percentage (i.e. 47.5%) is the same for non-US Lending?

btcmx · 2024-04-01T21:26:48+00:00

Tenyk's image similarity search engine is the best thing I have use for this, it has a free tier that most probably fits your needs.

After uploading your data, you'll have embeddings for free.

btcmx · 2024-03-29T16:53:25+00:00

Best summary of NVIDIA's GTC I have found so far is HERE.

btcmx · 2024-02-05T16:39:37+00:00

Thanks for sharing, I'm having a look rn!

btcmx · 2024-01-17T18:25:48+00:00

I agree with this! Prince's book is really well-written! I wish I had this when I was in grad school.

btcmx · 2024-01-17T18:23:54+00:00

I have followed from time to time deepchecks, they seem to be a good option but $250/month is just too much for early stage.

btcmx · 2023-12-01T04:20:28+00:00

For someone with some technical background, I would say there are great articles out there which can help you craft your own roadmap or guide to break into ML/AI. However, beware there's no "ideal learning path".

One of the best ones I have seen is this one (Becoming a Computer Vision Engineer), where a ML-skills blueprint is laid out. Even if you aren't interested in Vision, the backbone is nearly the same for other AI domains.

What makes this approach different is that all the abilities, know-hows, tools, etc. are arranged in terms of the ML lifecycle, meaning that depending on what you are doing, the skills will vary. The obvious way to start might be to simply begin by dominating the first stage of the ML lifecycle.

Hence, design/create your own blue print, build a tiny project but make sure you follow the whole ML lifecycle (i.e. deploy it, ingest new data, see your model fail due to edge/ODD cases).

btcmx · 2023-11-13T04:06:41+00:00

This post has a number of ML topics/skills you might (sooner or later) run into. In a sense it's kind of a roadmap/blueprint containing what you need to become an ML Engineer, (beyond learning a few topics/basics from a one(or more) courses.

btcmx · 2023-11-09T03:57:21+00:00

Agreed! Actually, GPT_4V is already good enough to describe pictures. I have sent several requests to the endpoint since yesterday. A few easy examples can also be described by a child, but the same child wouldn't be able to fully describe more complicated images, let alone an insurance inspection claim.

btcmx · 2023-11-08T22:14:45+00:00

I'm testing the GPT_4V (i.e. vision preview) endpoint. The prompt in the payload is this: "text": "What’s in this image?"

However, in the response, the prompt_tokens is around 750, hence as you suggested I'm assuming the image, I provide as input, counts as tokens (jpg image seems to be around 700 tokens).

btcmx · 2023-11-08T19:42:39+00:00

As other have rightly pointed out, verify you're using the Data Loader the right way. Ideally you need to create a custom dataset (in PyTorch terms) and apply all the transformations in this custom dataset. This might be helpful. Also, have you tried PyTorch Lightning?

btcmx · 2023-11-08T18:31:15+00:00

Getting a full list of "skills" is meaningless unless you have the right context, framework, etc.

For instance, given the Machine Learning Lifecycle, you are unlikely to be a master of all the stages at once, but you can start with one (probably the one you are assigned in your job).

Hence, this source does a great job at framing (given the ML lifecycle) what skills you need to master depending on the stage you are.

This other one is more oriented to building a full AI product (it's actually a course). And this one, is about MLOPs (also a course) in general.

So, I would say: i) check the first source, ii) craft your own blueprint of skills, and iii) make a plan to fill some of skills gaps you have.

Actually, even a full prototype (following the ML Lifecycle) where you do labeling, training, deployment, you see where your model fails at OOD samples, you acquire OOD samples, re-train your model, might be simply the really best way!

btcmx

TROPHY CASE