She is by far the best-looking authority on black holes. This was fun to listen to

*Summary* This presentation introduces Estimagic, a Python package for nonlinear optimization, highlighting its usefulness for complex estimation problems and statistical inference. *Key takeaways:* * *Problem (**0:40**):* Many professionals (e.g., epidemiologists, statisticians, engineers) face nonlinear optimization challenges but lack the expertise and interest to delve into optimization algorithms. * *Existing Solutions' Shortcomings (**1:25**):* Traditional optimization libraries like SciPy can be challenging for non-experts due to cryptic errors, boilerplate code, and difficulties switching between libraries with different interfaces. * *Estimagic's Solution (**3:51**):* * *Unified Interface:* Wraps optimizers from various libraries (SciPy, NLopt, etc.) under a common, user-friendly interface inspired by SciPy.minimize. * *Flexible Parameter Handling:* Accepts various parameter data types (NumPy arrays, Pandas objects, lists, dictionaries, etc.) and allows for nested structures, eliminating the need to manually unpack parameters. * *Diagnostic Tools (**7:46**):* Provides visualization tools (e.g., Criterion plot) to compare the performance of different optimization algorithms easily. * *Global Optimization (**9:32**):* Includes algorithms for global optimization (genetic algorithms, multi-start framework) to handle objective functions with multiple local optima. * *Other Features (**11:09**):* * Numerical derivative calculation * Maximization capabilities * Support for bounds and constraints * Jax compatibility for accelerated computations and automatic differentiation * Enhanced error handling for numerical stability * Algorithms for nonlinear least squares problems * *Team (**12:45**):* The speaker acknowledges the contributions of various individuals and institutions in developing and supporting Estimagic. * *Roadmap (**13:29**):* Future development plans include simplifying the addition of optimizers, creating algorithms for noisy optimization, and improving the real-time dashboard. * *Call to Action (**14:07**):* Users are encouraged to contribute, report issues, and request features on the Estimagic GitHub page. i used gemini 1.5 pro to summarize the transcript

The API is very similar to lpyspark. In fact I don't think it would be a hassle to convert existing pipelines to polars.

Excellent work! Solara is a joy to work with. Many thanks for creating it!

I have no knowledge or time to do benchmarking but, I was using pandas' "append" to combine about 8000 CSV files (about 10 GB in total) and it was taking almost an hour and a half, i decided to try polars, according to stack overflow i could use, concat, vstack, or extend, i randomly chose "vstack", and it did the same workload in less than 1 minute, same computer, same python version, same everything, all i had to do was modify the script a little bit, for example remove "index = False" when exporting the resulting (huge) dataframe to CSV.

impressive!

Am I being super Geeky cz im getting super excited by this😅😅

Thanks

At the beginning of the presentation he showed the performance of using only Pandas. How is the performance using Rust?

Very good progress. Looking forward to the day where predict_proba() is the default people use - not predict(). The automatic 0.5 decision threshold is basically never the right one!

Impressive, how scikit-learn is progressing, especially in the imbalanced learning area!

Love the move to industry use-cases!

Be the first to like ))) nice topic

It is a shame that we missed this material. An advanced talk with a horrible sound and image :( Can you please post the link to the slides?

Any updates on this work?

Very useful library!

Thanks, very useful.

Thank you for your nice talk! Roughly, how much longer do c-functions take to be implemented compared to their Numba counterpart?

Badly captured video. Can't see the projected screen

Thank you. I am using QuTip package in which i am making some logic gates. I follow the instructions In the user guide it is said that i need to install imagemagick but by conda or pip it does not install image magic but i installed wand.I already installed imagmagick on my pc manually. No after all the imports etc and after writing commands for gate making, i need to write gate.png to display the gate but it keep giving error below. Plz let me know what is this error and how can i display some result as image file in python. I will be grateful. I am using jupyter.

this is amazing, thank you!

Excellent

Here you can find the slides from the talk: www.slideshare.net/gakhov/exceeding-classical-probabilistic-data-structures-in-data-intensive-applications

Python's arithmetic operators, should be b = exp(-((x-mu)**2/(2*sigma**2))) b thanks for the great video.

Is there any code online available in this lecture?

I am using dask on kubernetes cluster on AKS (azure). I am getting some fixed number of workers . It is not zero. Where and what should I change?

Hey, very informative! Can you share the notebook?

can we have the ppt ?

hey, that's me! :)

You can find more information on "Scientific computing for quantum technology" and the other talks of EuroScipy 2018 at gist.github.com/carmelom/e4c898f2305f726aad744d0dd9bdf1a0.

kzbin.info/www/bejne/anLaiJywj5plm6s

Watching at 1.5x speed seems perfectly normal. Else it's super slow

github.com/jorisvandenbossche/talks/tree/master/2017_EuroScipy_geopandas

Greetings from Chile! Good job Seb!

Lovely presentation! Thanks, made for a lazy re-cap of PyTorch docs that I had been meaning to do.

Repos shown: github.com/aaron-xichen/torch-playground

Tutorial notebooks can be found here github.com/leosartaj/euroscipy-sympy-tutorial

bad audio!

Is it possible to download the Jupyter Notebook he is using somewhere?

@20:10 why had he made a sigmoid layer before the softmax layer, what i understand about logistic regression, is that it doesn't have a sigmoid activation

Fantastic talk!

See the summary at www.euroscipy.org/2017/descriptions/19828.html

Nice work, Windel!

Awesome stuff!

Great Job i love it

Seb❤️