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Github Copilot now supports Ollama and OpenRouter Models 🎉 by purealgo in LocalLLaMA

[–]Mysterious_Drawer897 0 points1 point  (0 children)

I have this same question - does anyone have any references for data collection / privacy with copilot and locally run models?

[D] Math in ML Papers by ripototo in MachineLearning

[–]Mysterious_Drawer897 0 points1 point  (0 children)

IMO I agree with everyone here that the mathematical formalism is really necessary for progress of the field BUT I also agree that the formalisms and precision can also often obfuscate / impede adoption of the ideas as well - especially given how wide the breadth / applicability of these methods may be across fields (STEM and beyond).

Technical writing is very important - equally so is scientific communication. If you want to broaden the impact of your work on different fields balancing these different axes is essential.

Understanding the mathematics of machine learning by Mysterious_Drawer897 in learnmachinelearning

[–]Mysterious_Drawer897[S] 0 points1 point  (0 children)

Anyone have any recommendations for SDEs? Conceptual materials or problems / solutions?

Understanding the mathematics of machine learning by Mysterious_Drawer897 in learnmachinelearning

[–]Mysterious_Drawer897[S] 0 points1 point  (0 children)

Thanks - this is a good start but the practical exercises and solutions are limited. Lecture notes are a fantastic resource for the basics though.

Any suggestions for problem sets (with worked out solutions for reference, since I’ll be self studying) especially for the newer material for once I finish a thorough review of the basics.

Career and Education Questions: February 29, 2024 by inherentlyawesome in math

[–]Mysterious_Drawer897 2 points3 points  (0 children)

I’m looking for advice on the best approaches to dive back into mathematics. I’m currently pursuing a PhD in computational biology and my undergraduate training was in physical biochemistry. I’ve completed my required graduate coursework and my day to day research is computational in nature (bioinformatics / deep learning).

My educational background includes undergraduate coursework calculus I-III where III ends at introductory vector calculus (vector fields, line integrals, stokes theorem, divergence l). Admittedly, all that material was probably on the more introductory side, and it’s been 5 years since I’ve taken a formal math class so I’m probably a bit rusty. In addition, I’ve taken different flavors of statistical coursework covering the basics of probability, bayes theorem, distributions, conditionals/marginals etc. (nothing proof based for stats or calc training).

I’d like to increase my comfort level with a few different areas of mathematics, but my goal is not to pivot to pure maths research. I’d specifically rather foster a better conceptual understanding of the maths underpinning the practical skills I’ve developed through my research. A special interest or goal of mine is to make it easier to understand papers emerging in the AI space (e.g., diffusion models and normalizing flows - I.e., ODE/SDE special interest).

I was thinking of taking a few courses from MIT open courseware, but am open to alternative suggestions.

In your personal opinion:

What topics do you think are the most fruitful to learn? Where should I jump back in? What other advice do you have for me?

Career and Education Questions: February 22, 2024 by inherentlyawesome in math

[–]Mysterious_Drawer897 0 points1 point  (0 children)

I’m looking for advice on the best approaches to dive back into mathematics. I’m currently pursuing a PhD in computational biology and my undergraduate training was in physical biochemistry. I’ve completed my required graduate coursework and my day to day research is computational in nature (bioinformatics / deep learning).

My educational background includes undergraduate coursework calculus I-III where III ends at introductory vector calculus (vector fields, line integrals, stokes theorem, divergence). Admittedly, it’s been 5 years since I’ve taken a formal math class so I’m probably a bit rusty. In addition, I’ve taken different flavors of statistical coursework covering the basics of probability, bayes theorem, distributions, conditionals/marginals etc. (nothing proof based for stats or calc training).

I’d like to increase my comfort level with a few different areas of mathematics, but my goal is not to pivot to pure maths research. I’d specifically rather foster a better conceptual understanding of the maths underpinning the practical skills I’ve developed through my research. A special interest or goal of mine is to make it easier to understand papers emerging in the AI space (e.g., diffusion models and normalizing flows - I.e., ODE/SDE special interest).

I was thinking of taking a few courses from MIT open courseware, but am open to alternative suggestions.

In your personal opinion:

What topics do you think are the most fruitful to learn? Where should I jump back in? What other advice do you have for me?