From a7741959942f145bef2faa8fe643a566a43b4ddb Mon Sep 17 00:00:00 2001 From: Nikhil Saldanha Date: Mon, 18 Sep 2023 08:11:57 +0200 Subject: [PATCH 1/4] add slide link of uncertainty viz --- .../12:00 - Uncertainty visualization with ArviZ/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md index bf4d14b..7f9a8ea 100644 --- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md +++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md @@ -1,5 +1,6 @@ # Uncertainty visualization with ArviZ by Oriol Abril Pla * [Talk info](https://amsterdam2023.pydata.org/cfp/talk/DH3N3R/) +* [Link to Slides](https://oriolabril.github.io/visualitzacio_incertesa/en.html) ## Abstract Learn how to visualize uncertainty in parameters or predictions using mutiple visualizations adapted to your data and task From 6aa27977ba41729199c0d5779d1d27a8e337a192 Mon Sep 17 00:00:00 2001 From: Nikhil Saldanha Date: Mon, 18 Sep 2023 08:14:11 +0200 Subject: [PATCH 2/4] add link to slide content - designing ml system --- .../10:30 - Designing a Machine Learning System/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md index c3ca5e3..c447977 100644 --- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md +++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md @@ -1,5 +1,6 @@ # Designing a Machine Learning System by Jetze Schuurmans, Roy van Santen * [Talk info](https://amsterdam2023.pydata.org/cfp/talk/N8DYS7/) +* [Link to slide content](https://xebia.com/blog/how-ml-system-design-helps-us-to-make-better-ml-products/) ## Abstract Are you a machine learning practitioner struggling with designing, reasoning, and communicating about ML systems? Then this session is for you! With the industry moving towards end-to-end ML teams to enable them to implement MLOps practices, it is paramount for you to understand ML from a systems perspective. In this hands-on session, you will gain a thorough understanding of the technical intricacies of designing valuable, reliable and scalable ML systems. 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zn7+2=VpNX+CiVCPrjnn;j^nQN)idaj@U#Aa(eoREPSOhoYOGU0T+0Pt(ed`xpOA&( z4Oc#@%>hGiM;ePe-uJ!~O%3iN0+Z^P0^@qDz|f#3`81J&FoqC`_5*xjy%HTiXDQ@k zy9b5_7wH}t!eE{c)=Vt(49dias2pV=P(@WcLUt;iHMK zZsG?iGRC$hf=7yNg?#X`zGFegwPw)Sc&yi#PGDTOK+w+5F^VT35lHxsMO+M5Nf-+A zlsju!8k{lHA*hyQ#;K8g>(16?nY1sGh1?|kTtaSkTNq9e+5 z2nv}m@?ZA%n2(=*!+c1~=xm|k3SpQB;wFhNL?PR7OQ5_Cv_2)7k(cql3BD9xA@@Ui z$+92?qZkQv5QS__R0R3_3XI}=JS2!Kiq~NxBjk19?%_KCKcvEgX{0N7dxZb@gehw734EB=F7RG9ld>R_NG7pZ?F&`=%v|eDcEa=7XnE@TD z|D+dKF5f9o0RG2x=vgxcugG_v(15gFJovyi1Ue+K5)T||!bItiYzW;2R9YoE&dU%L zBN=Di+juZd9*d_EXg=G7BO(k*BHxX8;8XG&qC`fqH=+pan Date: Tue, 26 Sep 2023 13:35:41 +0200 Subject: [PATCH 4/4] Add link to probabilistic predictions with sktime --- .../README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md index 68680fa..0d9998d 100644 --- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md +++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md @@ -1,7 +1,8 @@ -# Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro -by sktime community -* [Talk info](https://amsterdam2023.pydata.org/cfp/talk/F8EW7P/) -## Abstract +# Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro +by sktime community +* [Talk info](https://amsterdam2023.pydata.org/cfp/talk/F8EW7P/) +* [Link to Tutorial](https://github.com/sktime/sktime-tutorial-pydata-Amsterdam-2023) +## Abstract Probabilistic predictions are predictions that include some statements about uncertainty of the prediction, e.g., prediction intervals that make statements about a likely range of values that a prediction can take. This workshop gives an introduction on making probabilistic predictions with the sktime and skpro python packages, for forecasting and supervised regression. Both packages are sklearn-compatible, built using skbase, with composable and modular interfaces. -The presentation includes a practical primer of different types of probabilistic predictions, algorithms and estimators, and evaluation workflows, with python code examples. +The presentation includes a practical primer of different types of probabilistic predictions, algorithms and estimators, and evaluation workflows, with python code examples.

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