Hey guys, I’m Alim. I work as a technical support associate in IBM and I have a BE in mechanical. Im looking towards shifting to machine learning as this is what interests me a lot and I’ve really been wanting to work in this field.

I wanna know what would be the best approach I could take in order to slowly learn and get better at it and eventually be able to work with maximum efficiency. I have a lot of time on my hands as in I have got around a year before I can apply for internal job postings within this field. So it would be great if anybody could suggest me a course and things to do which would help me.

Thanks in advance. Have a wonderful day! 🙂

Hi all !

My company has a Keras project where the deployment is currently handled by a homemade Flask API and custom bash scripts. We’re having trouble

• versioning our code, models and data,
• going from experiments to production-ready code,
• designing clear standards and automatic release checks.

We’re looking for one (or several combined) ML framework that would help us solve these issues. So far we’re benchmarking TFX, MLFlow, Floydhub, Acumos, Neptune, DVC and Pachyderm, but we wonder if we didn’t miss a good candidate.

Did a framework change your life ? Share your experience !

Does it really matter what I major in (CS or Stat) for my undergrad degree, especially if I plan on doing a MLE or Data Science program in grad school? Would it be more beneficial to have a particular major/minor combination (i.e. major in CS and minor in math/stat) now?

I got the chance to have an interview with an online ads company. The interviewer asked me a question

“if we expect a newly trained model to perform well in online exp, but exp result is pretty negative, how to debug? “

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My answer is “may be caused by overfitting. if so, can change the models, e.g. if using decision tree, can switch to random forest”.

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The interviewer seems not very satisfied with the answer as he says switching model is heavy weight. I then answered that it could be feature or data distribution discrepancy. Then he asked how to debug these two cases. I am a little stuck.

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Want to know some of your opinions?

The Glove paper states the following:

Our final model should be invariant under this relabeling, but Eqn. (3) is not.

Why is Equation 3 not invariant under the relabeling?

Which relabeling are they referring to?

Thanks!

I’m reading this https://arxiv.org/pdf/1705.03122.pdf

On page 3, 2nd paragraph I don’t understand what W and b_w are. I get that the inputs are a (k x d) matrix, but how is the convolution performed and why is the output Y size (1 x 2d)?

Hi,

I am sharing the problem I face in Matlab but if you have a solution for this problem even in Python then I would very very happy.

I was able to estimate conditional probability (CDF) for a dataset that has two features (X_1 and Y) i.e., P(X_1|Y) using a Matlab function called “quantilePredict”. It works great. However, when I consider three features X_1, X_2 and Y. Then how can I find the P(X_1,X_2|Y) without the assumption of conditional independence?

How to capture the covariance as well as the CDF while considering quantiles but not mean of the data? Worst case I am fine with how to capture the covariance as well as CDF with mean of the data?

TreeBagger is trained (f) by giving “Y” as input and X_1 as output i.e., X_1 = f(Y). We then use the Treebagger model to predict responses for “quantilePredict” but in multivariate case, the Treebagger cannot fit the data where the input is “Y” and output has “X_1, X_2” i.e., Y = f(X_1,X_2) (this idea/pov is probably wrong and naive) ?.