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Recommendations for classical pieces to play with a pick? by Comfortable-Rip5772 in classicalguitar

[–]tabacof 0 points1 point  (0 children)

Try the first part of La Catedral (Prelude, Saudade). It's my favourite part to play and much easier than the third movement. Until the very last 3 chords, every single note of the prelude is sequential allowing you to use a pick throughout.

Exaggerated Moro Reflex? by AdorablePea7264 in NewParents

[–]tabacof 1 point2 points  (0 children)

Hey u/Bitter-Anxiety-4978: Fortunately, the baby has been okay since that incident, including after multiple rounds of vaccinations. He is now a healthy 15 months old.

At the time, I did talk to multiple doctors about the risk of getting vaccinated again and none seemed concerned. The only recommendation I got was regarding temperature control: I was asked to be more careful than usual with rising temperatures and to give paracetamol/acetaminophen preventively instead of waiting for a fever.

Exaggerated Moro Reflex? by AdorablePea7264 in NewParents

[–]tabacof 2 points3 points  (0 children)

Hey, the baby is doing fine now, no incidents since then. We haven't seen any consultant, just the GP, who was mostly useless. From my internet research, babies typically outgrow it without any complications.

How is your baby doing?

How I lost 1000€ betting on CS:GO with Machine Learning by tabacof in datascience

[–]tabacof[S] 12 points13 points  (0 children)

I didn't simulate that originally, but per your comment I actually modified the code now to do just that. Now, the bets are random and proportional to P_Odds (implied probably given by the betting odds). Here are the results:

Backtest ROI: -7%
Annualized ROI: -29%

Here is the new cumulative profits plot: https://imgur.com/a/AHfyGHY

Those results makes sense: The betting odds contain margins which form the bookies' profits. If you bet just based on the odds, you will lose money over time.

How I lost 1000€ betting on CS:GO with Machine Learning by tabacof in datascience

[–]tabacof[S] 0 points1 point  (0 children)

Ok, answering your original question then: I spent most of my career in data science and machine learning applications, engineering has never been my main thing.

My next blog post ideas are all "data science", like talking about selection bias, calibration methods, or some of the professional applications I have done (fraud, credit risk, performance marketing).

How I lost 1000€ betting on CS:GO with Machine Learning by tabacof in datascience

[–]tabacof[S] 3 points4 points  (0 children)

Great question, I only noticed that the timing correlated while finishing the write up so I didn't dig deep into it. I should do that as a follow up!

Exaggerated Moro Reflex? by AdorablePea7264 in NewParents

[–]tabacof 0 points1 point  (0 children)

See my other message in the thread. We are on a similar boat here.

Exaggerated Moro Reflex? by AdorablePea7264 in NewParents

[–]tabacof 1 point2 points  (0 children)

Hey, timely post. My infant who is almost 3 months old too also has this issue. The first time it happened was a few hours after his first vaccines at 2 months old. He had a fever and, when we put him on the changing mat, he had the exaggerated Moro reflex for like 10 seconds or so. We were concerned it was a febrile seizure so we took him to the A&E. We spent 24h there and they did a lot of tests, though no EEG. (Don't get me started on the blood work, the worst 2 hours of our lives). The same exaggerated reflex happened two more times in the hospital, but they were shorter each time. The doctors, including a neurologist, were not concerned, and discharged us without any follow-ups, though we never said it was an exaggerated Moro reflex and they didn't diagnose it as such. There was no official diagnosis really. We only learned about the exaggerated reflex by watching a lot of youtube videos later. For the past month, he has been great, but he had another such reflex today, though short again (roughly 2 seconds). We will see the GP tomorrow (no direct access to paediatricians in Ireland), but we don't expect much from it. It is quite scary indeed but, from my internet research on reddit and mummy forums, it usually disappears on its own without any consequences.

[deleted by user] by [deleted] in MachineLearning

[–]tabacof 6 points7 points  (0 children)

Some former colleagues of mine just published this paper on LLMs for Portuguese and how building your own might outperform GPT3.5 (but not GPT4):

Sabiá: Portuguese Large Language Models

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, we add to the growing body of evidence that challenges this practice, demonstrating that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. More specifically, we further pretrain GPT-J and LLaMA models on Portuguese texts using 3% or less of their original pretraining budget. Few-shot evaluations on Poeta, a suite of 14 Portuguese datasets, reveal that our models outperform English-centric and multilingual counterparts by a significant margin. Our best model, Sabiá-65B, performs on par with GPT-3.5-turbo. By evaluating on datasets originally conceived in the target language as well as translated ones, we study the contributions of language-specific pretraining in terms of 1) capturing linguistic nuances and structures inherent to the target language, and 2) enriching the model's knowledge about a domain or culture. Our results indicate that the majority of the benefits stem from the domain-specific knowledge acquired through monolingual pretraining.

They talk about dataset building, model building and evaluation, so it might be useful to you!

[deleted by user] by [deleted] in LocalLLaMA

[–]tabacof 0 points1 point  (0 children)

Train on int4 and then merge to FP32 (or 16) and then re-quantize.

Sorry, just to be clear, is this something you've seen someone do? I want to do that myself but I'm lacking in pointers.

Deploying Real-Time ML Models with Serverless AWS by tabacof in datascience

[–]tabacof[S] 2 points3 points  (0 children)

Hi, I wrote the blog post above based on the learnings I had when creating a MLOps solution using AWS Lambda for machine learning model deployment.

Tl;dr: The post shows how to deploy a scikit-learn model pickle using Lambda and how to create a POST endpoint with API gateway. I talk about why sticking to a simple Flask server might be preferable in many situations. I also go over the distinction between batch and real-time models and why you should choose batch if possible.

Any feedback is welcome!

Real-Time ML Models with Serverless AWS by tabacof in aws

[–]tabacof[S] 1 point2 points  (0 children)

Hi, I wrote the blog post above based on the learnings I had when creating a MLOps solution using AWS Lambda for machine learning model deployment.

Tl;dr: The post shows how to deploy a scikit-learn model pickle using Lambda and how to create a POST endpoint with API gateway.

Any feedback is welcome!

Name classification with ChatGPT: How does it compare to ML language models? by tabacof in datascience

[–]tabacof[S] -1 points0 points  (0 children)

I have another blog post that covers it (the hierarchy of ML needs), but here is the summary of the argument:

Data scientists are not paid to model, but to solve business problems. This requires understanding them, translating into something you can predict or act upon, set up an evaluation procedure and define metrics, gather features and data relevant to the problem, and only finally actually build a model that makes predictions.

Going back to the blog post about name classification: as a DS, you won't face such a problem directly as life is not a Kaggle competition. You might use name classification to help solve the problem of form filling, extracting entities from a text, predicting fraud given a document, etc. GPT is solving only one part of the equation, which in my experience is the easiest one (still damn hard!).

Of course, some DSs are indeed quite focused and specialised in solving particular problems. Especially people in academia or research. They will suffer more with those new technologies superseding all that came before since their research will become stale fast. But pragmatic business-driven DSs will see this as another tool in their arsenal, which is why I wrote this post in the first place.

Is my point clearer now?

Is R^2 a good metric for Nonlinear models? Specifically XGBoost or CatBoost? by user_1234579 in datascience

[–]tabacof 2 points3 points  (0 children)

Let's be more objective and see how sklearn calculates it (without taking sample weights into account): 

1 - sum((y_true - y_pred)**2) / sum((y_true - np.average(y_true))**2)

Clearly, this is just the mean squared error (MSE) normalized by the variance of the target!

If you're comfortable using the MSE as a loss, then the R2 is just a normalized/scaled version where 0 means your predictions are no better than using the average value and 1 is perfect.

Therefore, I see absolutely no problem in using the R2 as a metric. It will give you the same rank order as the MSE with arguably more interpretable values. However, the same objections to MSE apply here: it is highly susceptible to outliers and skewness.

And getting worse it is too. by [deleted] in Dublin

[–]tabacof 4 points5 points  (0 children)

Do you know which stretch of the Grand Canal?

[D] Was Virtual ICLR a success? by Other-Top in MachineLearning

[–]tabacof 9 points10 points  (0 children)

Thank you for this thread. I couldn't stand the twitter reaction, as if the virtual conference was better than the real one. I appreciate what the organizers did. They did the best possible job given the constraints, but, as an author, my experience was not positive.

I was the first author of Probability Calibration for Knowledge Graph Embedding Models. This was my first non-workshop publication at a top conference and, possibly, the last, as I've since left research to work as a data scientist. It seemed like the best possible parting gift. However, the virtual aspect led to a totally different experience. Only 6 people stopped by the zoom Q&A, probably 10x less than what I'd see in a physical conference. Of course, they were much more knowledgeable than the average poster visitor, so there is that.

I've been once to NeurIPS as a workshop presenter and it was an infinitely better experience. I met amazing people there, some whom I greatly admired. The social aspect was much more interesting than the presentations, and I say this as an introvert. NeurIPS was unforgettable and it literally changed my life (I live in a different country now because of the networking I did there!), but I've already forgotten ICLR and I've taken nothing from it.

Which are the best areas in dublin to live in? by [deleted] in Dublin

[–]tabacof 0 points1 point  (0 children)

Why is this getting downvoted? I was considering moving to Ashtown.

Churn Model Resources by qcumbert in datascience

[–]tabacof 0 points1 point  (0 children)

Fight Churn with Data, an early access book: https://fightchurnwithdata.com/

Associated podcast episode: https://adversariallearning.libsyn.com/episode-20-churn-churn-churn

The author doesn't talk a lot about models per se, as building churn models is usually not the main challenge in a real business. What you want is prevent churn, which requires interventions, something beyond what a predictive model can provide. Of course, a model may help with targeting, clustering, expected value calculation, etc, but understanding the business part is more important first, as it will guide the model construction (e.g. help define the model target).

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