Machine learning and other gibberish
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
#ml
Fotios Petropoulos initiated the forecasting encyclopaedia project. They published this paper recently.
Petropoulos, Fotios, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, et al. 2022. “Forecasting: Theory and Practice.” International Journal of Forecasting 38 (3): 705–871.
https://www.sciencedirect.com/science/article/pii/S0169207021001758
Also available here: https://forecasting-encyclopedia.com/
The paper covers many recent advances in forecasting, including deep learning models. There are some important topics missing but I’m sure they will cover them in future releases.
Fotios Petropoulos initiated the forecasting encyclopaedia project. They published this paper recently.
Petropoulos, Fotios, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, et al. 2022. “Forecasting: Theory and Practice.” International Journal of Forecasting 38 (3): 705–871.
https://www.sciencedirect.com/science/article/pii/S0169207021001758
Also available here: https://forecasting-encyclopedia.com/
The paper covers many recent advances in forecasting, including deep learning models. There are some important topics missing but I’m sure they will cover them in future releases.
#career
> so the job of data scientist will only continue to grow in its importance in the business landscape.
>
> However, it will also continue to change. We expect to see continued differentiation of responsibilities and roles that all once fell under the data scientist category.
https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century
> so the job of data scientist will only continue to grow in its importance in the business landscape.
>
> However, it will also continue to change. We expect to see continued differentiation of responsibilities and roles that all once fell under the data scientist category.
https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century
#python
Guidelines for research coding. It is not the highest standard but is easy to follow.
https://goodresearch.dev/
Guidelines for research coding. It is not the highest standard but is easy to follow.
https://goodresearch.dev/
#ml
https://arxiv.org/abs/2205.02302
Kreuzberger D, Kühl N, Hirschl S. Machine Learning Operations (MLOps): Overview, definition, and architecture. arXiv [csLG]. 2022 [cited 17 Jul 2022]. doi:10.48550/ARXIV.2205.02302
https://arxiv.org/abs/2205.02302
Kreuzberger D, Kühl N, Hirschl S. Machine Learning Operations (MLOps): Overview, definition, and architecture. arXiv [csLG]. 2022 [cited 17 Jul 2022]. doi:10.48550/ARXIV.2205.02302
#ml
The recommended readings serve as a good curriculum for transformers.
https://web.stanford.edu/class/cs25/index.html#course
The recommended readings serve as a good curriculum for transformers.
https://web.stanford.edu/class/cs25/index.html#course
#fun
😂😂😂
[P] No, we don't have to choose batch sizes as powers of 2: MachineLearning
https://www.reddit.com/r/MachineLearning/comments/vs1wox/p_no_we_dont_have_to_choose_batch_sizes_as_powers/
😂😂😂
[P] No, we don't have to choose batch sizes as powers of 2: MachineLearning
https://www.reddit.com/r/MachineLearning/comments/vs1wox/p_no_we_dont_have_to_choose_batch_sizes_as_powers/
#ml
Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2019. doi:10.1145/3287560.3287596
https://arxiv.org/abs/1810.03993
Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2019. doi:10.1145/3287560.3287596
https://arxiv.org/abs/1810.03993
#ml
This is also like one thousand years later...
PyMC 4.0 Release Announcement — PyMC project website
https://www.pymc.io/blog/v4_announcement.html
This is also like one thousand years later...
PyMC 4.0 Release Announcement — PyMC project website
https://www.pymc.io/blog/v4_announcement.html
#data
If you are building a simple dashboard using python, streamlit is a great tool to get started. One of the problems in the past was to create multipage apps.
To solve this problem, I created a template for multipage apps a year ago.
https://github.com/emptymalei/streamlit-multipage-template
But today, streamlit officially introduced multipage support. And it looks great. I haven’t built any dashboards for a while, but to me, this is still the go-to solution for a dashboard.
https://blog.streamlit.io/introducing-multipage-apps/
If you are building a simple dashboard using python, streamlit is a great tool to get started. One of the problems in the past was to create multipage apps.
To solve this problem, I created a template for multipage apps a year ago.
https://github.com/emptymalei/streamlit-multipage-template
But today, streamlit officially introduced multipage support. And it looks great. I haven’t built any dashboards for a while, but to me, this is still the go-to solution for a dashboard.
https://blog.streamlit.io/introducing-multipage-apps/
#fun
Higharc is a start-up helping people design houses using generative designs.
The demo looks amazing.
https://higharc.com/
Higharc is a start-up helping people design houses using generative designs.
The demo looks amazing.
https://higharc.com/
This is hilarious.
Source:
https://mobile.twitter.com/arankomatsuzaki/status/1529278580189908993
Paper: https://arxiv.org/abs/2205.11916
#ml
I have heard about deepeta before but never thought it was a transformer.
According to this blog post by uber, they are using an encoder decoder architecture with linear attention.
This blog post also explains how they made a transformer fast.
DeepETA: How Uber Predicts Arrival Times Using Deep Learning
https://eng.uber.com/deepeta-how-uber-predicts-arrival-times/
I have heard about deepeta before but never thought it was a transformer.
According to this blog post by uber, they are using an encoder decoder architecture with linear attention.
This blog post also explains how they made a transformer fast.
DeepETA: How Uber Predicts Arrival Times Using Deep Learning
https://eng.uber.com/deepeta-how-uber-predicts-arrival-times/