seiqooq

seiqooq t1_iw82elw wrote

Reply to comment by badhandml in [D] ML/AI role as a disabled person by badhandml

It happens, there are a million reasons candidates get rejected, only several of which are reasonable.

Two of my ML job offers were extended through acquaintances I met in career development groups (ML meetups). I always recommend these, particularly now that they’re mostly online.

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seiqooq t1_it443kx wrote

Reply to comment by Narigah in [D] Simple Questions Thread by AutoModerator

I think you’re just about there with an answer. Assuming each occurrence is weighted evenly you could approach this a few ways:

  1. Use binary labeling such that the output vector looks like [0,0,0,01,0,0…, 1] and is of length 350. You can think of this as representing the true goal of finding the exact positions. Then, during optimization, you can determine a threshold or other logic to handle all of the fuzzy predictions that will inevitably result from training.

  2. Assign fuzzy labels scaling inversely with the distance from the target point. EG [0, 0.1, 0.5, 1, 0.5, 0.1, 0…]. The same thresholding can be done here as well.

Assuming locale is important for classification, I’d consider using convolutions as well to extract useful information from neighboring data points.

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seiqooq t1_it3zp9b wrote

Reply to comment by Unusual_Variation_32 in [D] Simple Questions Thread by AutoModerator

It’s useful to think of regularization simply as offering a way to punish/reward a system for exhibiting some behavior during training. Barring overfitting, if this leads to improvements in training error, you can expect improvements in test error as well.

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seiqooq t1_it3wkzy wrote

Reply to comment by EManO13 in [D] Simple Questions Thread by AutoModerator

Try to think of this in terms of how you will use the model. It sounds like a day-trading model, correct me if I’m wrong. In this case, you’ll want to ask the question of “based on todays trading patterns, should I sell now, or is the peak still likely to come?”.

See if this helps your problem formulation and therefore your labeling.

As a side note, most models are not sophisticated enough to capture the extreme complexity of stock behavior. If this is your first foray into stock prediction, I’d recommend tempering expectations.

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seiqooq t1_it3ixpk wrote

Reply to comment by VoyagerExpress in [D] Simple Questions Thread by AutoModerator

Correct me if I’m wrong but you say you’d like to improve your SNR up to some value, it sounds like you could simply formulate this as a 1D maximization problem, rather than a 2D optimization problem. In this case, reinforcement learning and genetic algorithms are high on the list as solutions.

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seiqooq t1_isn62an wrote

Reply to [R] Embedding dates ? by MichelMED10

What is your ground truth? How does the data available for prediction differ from your GT? Depending on your answers, dates may add noise to your predictions.

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