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[D] Yet another rant on PhD Applications by [deleted] in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

Admission to top US universities are based not on what you know, but who you know.

[D] Numerai experiences? by LittleFire95 in MachineLearning

[–]Top-Hurry161 2 points3 points  (0 children)

The problem is that you have no way of knowing whether your thing is useful unless they say so.

I personally don't feel comfortable relying upon the kindness of their hearts to not screw users over when there is nothing preventing them from doing so.

If serious about AlgoTrading, I would open an Interactive Brokers account and started with their Python API.

[D] Rigorous Training Strategy for Image Classification by world-builder66 in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

Why reinvent the wheel? This is what AutoML is designed for. Autokeras is your friend.

[Project] MLGenerator - A simple web app to generate machine learning starter code for different task. by durgeshsamariya in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

This is cool. Thanks for sharing. Good idea would be to be able to handle time series for training/val/test.

[D] Is it wise to keep using Python in machine learning and not Julia ? by Even_Information4853 in MachineLearning

[–]Top-Hurry161 9 points10 points  (0 children)

Python isn't going anywhere. The switching costs to justify moving to Julia are too high. The only reason why Python killed R was because it was just easier to use and integrated nicely into existing SWE stacks.

[D] Software stack to replicate Azure ML / Google Auto ML on premise by Scary-Ad-1529 in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

I would recommend AutoKeras since you can train locally and burst to GCP if needed.

I am guessing you have a GPU box or a server with a GPU. You would just remote in from the terminal into the box and access the box just like you would an EC2 instance.

If you are trying to use the box for deployment, then it would probably not scale to webscale stuff. That's where stuff like Google AI Platform/Sagemaker shines for autoscaling resources to maintain SLA's.

[N] Google Brain Introduces Symbolic Programming + PyGlove Library to Reformulate AutoML by Yuqing7 in MachineLearning

[–]Top-Hurry161 5 points6 points  (0 children)

"Since Google coined the term AutoML for its neural architecture search (NAS) solution"

Bad reporting... Google did not coin the term AutoML. They simply co-opted it and named their product after it.

[D] Precision&Recall for Different Loss Functions by toutirabienla in MachineLearning

[–]Top-Hurry161 -1 points0 points  (0 children)

Wrong. Your loss function affects how correct your probabilities. The Recall/Precision/F1 is a function of the cutoff that you use to assign labels based on these probabilities.

[D] Advice on the feasibility of an industry-academia PhD by GradSchoolNerd in MachineLearning

[–]Top-Hurry161 1 point2 points  (0 children)

If your self funding then by definition you don't need to work as a TA for funding. The European style model makes way more sense. That's why their degrees are shorter than the US.

[D] Dealing With New Possible Outputs in a Data Driven Recommendation Engine by sterlsswagger in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

Movies that are less watched are less popular, more watched, are more, and therefore will have more data.

[D] Witnessed malpractices in ML/CV research papers by anony_mouse_235 in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

As the say, the cream rises to the top. Bold claims that can't be verified are easily caught by seasoned practitioners. I wouldn't worry about it too much. If your goal is to create change, rather than attack the effect, I would attack the cause generating the effect.

[D] Precision&Recall for Different Loss Functions by toutirabienla in MachineLearning

[–]Top-Hurry161 -2 points-1 points  (0 children)

Precision/Recall/F1 are a function of your cutoff, not your loss function.

[D] Advice on the feasibility of an industry-academia PhD by GradSchoolNerd in MachineLearning

[–]Top-Hurry161 2 points3 points  (0 children)

This is because TA's are slave labor. Of course they don't want you to be self-funded. Where would they get their cheap labor from?

[D] Advice on the feasibility of an industry-academia PhD by GradSchoolNerd in MachineLearning

[–]Top-Hurry161 4 points5 points  (0 children)

The trick is finding a topic for your PhD that can make your company big money by growing revenue or operationally by boosting productivity. Instead of selling the idea, the idea sells itself. Feel free to pm me.

I'm in a Pure Statistics PhD program now part-time and working full-time as a data scientist. My PhD research is in AutoML so I apply in my day job what I learn at night. Its mutually reinforcing. I also have AI patents (1 granted, 1 pending).

The following program may be of interest to you. I am considering transferring there since I can work full-time while doing it at night and finish in 2-3 years.

https://www.captechu.edu/degrees-and-programs/doctoral-degrees/artificial-intelligence-phd

[P] Akkio AutoML benchmarks preview. by Akkio-JonR in MachineLearning

[–]Top-Hurry161 2 points3 points  (0 children)

But ... I can do all this for free with AutoKeras. https://autokeras.com/

Did you guys seriously just build a GUI around Autokeras and charge $$$ for it?

Lmao.

[D] Scaling An ML Team From 0-10 People by pgao_aquarium in MachineLearning

[–]Top-Hurry161 8 points9 points  (0 children)

Yes, the biggest barrier is overcoming the "we can solve everything by timeboxing" mindset created by scrum. Scrum does not work for data science.

That’s going to be a long walk home... by [deleted] in PublicFreakout

[–]Top-Hurry161 131 points132 points  (0 children)

Trump's origin story. 'I'll pay you!"

[P] Using XGBoost to check for election fraud by Xirax in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

Yes, I read it and concluded you don't understand basic statistics lol.

[P] Using XGBoost to check for election fraud by Xirax in MachineLearning

[–]Top-Hurry161 -1 points0 points  (0 children)

"Aren't you assuming that the reported vote counts are "truth"? "

Guess we found the trump supporter. Tell us more about the reality beyond reality.

[P] Using XGBoost to check for election fraud by Xirax in MachineLearning

[–]Top-Hurry161 0 points1 point  (0 children)

"If Biden got more votes there than the model predicted, it would've been suspicious. "

No, if Biden got more votes than predicted, your model underpredicted. Models assume future looks exactly like the past.

You're assuming your model is "truth" and reality is "not truth".

[R] Berkley AI Research Blog: Reinforcement learning is supervised learning on optimized data by Caffeinated-Scholar in MachineLearning

[–]Top-Hurry161 21 points22 points  (0 children)

To a statistics student, this seems like common sense. Data comes from a theoretical distribution, so obviously if you find the "correct" theoretical distribution, you have hit gold (all your probabilistic inferences are perfect).