William Smith

Happy to see Kameleoon’s customer, Coop, taking part in Ron Kohavi, Jakub Linowski, and Lukas Vermeer’s challenge! Do button tests really make a difference? Joachim Furuseth, CRO Manager at Coop Norway, investigated this by A/B testing round vs. square CTAs. His conclusion? “Don’t trust anything that seems too good to be true—it probably is!” After analyzing data from 4 million users, the results showed a negligible uplift of just 0.56%—100 times smaller than anticipated. Round or square buttons? They don’t significantly impact conversions, at least not according to the data. Curious for more insights? Check out Ronny’s post below! 👇

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"In this article, I’ll talk about the core challenges and demonstrate developing a simple agent in an evaluation-driven way using PydanticAI." 🖋️ by Valliappa Lakshmanan

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Join us on May 29, 2024, for a webinar covering deep learning frameworks on Argonne National Laboratory's Aurora exascale supercomputer. In this developer session, ALCF's Corey Adams will discuss deep learning frameworks optimized for Aurora, including TensorFlow, PyTorch, and the experimental release of JAX. Other topics include how to achieve optimal single device performance, as well as scaling up and scaling out models. The session will include examples and demonstrations that can run on Sunspot or Aurora. https://lnkd.in/gY2WMZ3x

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Imputing Missing Data in Categorical Variables --Discussing KNeighborsClassifier, SimpleImputer and CategoricalImputer Methods https://lnkd.in/gidTq4hg

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🧠 For my folks who are interested in learning more about programming in R: Harvard's CS50 is offering a free course on Introduction to Programming with R on edX! This is a great FREE opportunity to level up your data skills in a powerful language for statistics and data visualization. 📈 Whether you're new to coding or looking to add R to your toolkit, this self-paced course is a great place to start! #CS50 #RProgramming #DataScience #LifeLongLearner

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Why is clustering analysis so hard? In his paper “an Impossibility Theorem of Clustering” (2002) Jon Kleinberg articulated that there is no clustering model that can satisfy all three desirable axioms simultaneously: namely, scale invariance, richness, and consistency. (Kleinberg, 2002) To cut a long story short, Kleinberg demonstrated that a mathematically satisfactory clustering algorithm simply does not exist. This might be a death sentence for clustering analysis to some theoretical fundamentalists. Nevertheless, ever since Kleinberg released his impossibility theorem, many methodologies for clustering evaluation have been proposed from the field of engineering (e.g. applied mathematics, information theory, etc.). To pursue pragmatism in filling the gap between theoretical/scientific limitations and practical functionality is the domain of engineering. That said, easier said than done. As Kleinberg’s Impossibility Theorem suggests, it is very hard to evaluate results of clustering algorithms. As a matter of fact, different validation techniques could yield confronting assessments about the same clustering results under investigation. In addition, evaluation metrics requires a certain set of pre-requisites in order to generate any meaningful information: in other words, a violation of one single pre-requisite can impair the validity of the metric. Remember this famous aphorism: “All models are wrong, but some are useful.” (https://lnkd.in/dj_xj69T )When we use any model, this caveat should echo and resonate in our mind. In this context, I encountered a paper written by Julio Omar P. & Fernando Berzal (2019), “Evaluation Metrics for Unsupervised Learning Algorithms” ( https://lnkd.in/dj_xj69T ) in which they proposed a clustering validation framework in their attempt to better evaluate the quality of clustering performances under the mathematical limitations posed by “the Impossible Theorem”. To promote our pragmatic use of clustering algorithms, I thought that it would be constructive to share my study note about their evaluation framework in my new article at Towards Data Science. Given the imperfect conditions under which we live in, let’s promote practical knowledge with vigilance in the spirit of Aristotelian Phronesis (https://lnkd.in/dKnihn9g.). #clustering #validation #machinelearning #datascience #pragmatism #phronesis #evaluation #metrics #impossible #trilemma #violation #engineering #imperfection #impossibility https://lnkd.in/dWtuRTPi

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Organizations are turning to open source foundation models for control and ability to better influence outputs by more tightly managing both the models and the data they are trained on. As a response, we developed DBRX, a state-of-the-art open LLM that enables organizations to utilize their own data to create their own LLMs ⬇️ https://dbricks.co/4b8uRTy

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Added to my must read list.

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Commonly used terms in #data preprocessing prior to machine learning are rescaling, normalizing, and standardizing.  These terms are used more or less interchangeably depending on the customs within various fields. 'Normalizing' a vector most often means dividing by a norm of the vector, for example, to make the Euclidean length of the vector equal to one (unit norm). 'Normalizing' also refers to rescaling by the range of the vector (minimum & maximum), to make all the elements lie between 0 and 1. ⭐ https://lnkd.in/dsXS_sey 'Rescaling' a vector means adding or subtracting a constant, then multiplying or dividing by a constant, as one would do to change the units of measurement of the #data. 'Rescaling' also refers to scaling with median and quantiles, which is called robust scaling (rescaled #data becomes robust to outliers). ⭐ https://lnkd.in/deSb2yb8 'Standardizing' a vector most often means subtracting a measure of location and dividing by a measure of scale. For example, if the vector contains random values (with Gaussian distribution), one subtracts the mean and divides by the standard deviation, thereby obtaining a "standard normal" random variable with zero mean and unity standard deviation. ⭐ https://lnkd.in/dEpyrsGx And these essential-to-know feature scaling methods have been nicely elaborated by Soledad Galli in one of her blogs at Train in Data. Thanks to her! Other methods apart from above for #data preprocessing with #scikit-learn are available here. ➡ https://lnkd.in/dtnbhpbt

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Speculative Sampling is the principle of using two language models simultaneously to produce output faster at a reduced cost. https://buff.ly/3WAGKNq Author: https://buff.ly/3JVGHEn

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【Script-Based GPU-Ready ELM Development for Continuous Code Integration】 Full article: https://lnkd.in/gFM7gFdQ (Authored by Peter Schwartz, et al., from Oak Ridge National Laboratory, Environmental Sciences Division (USA), etc.) The E3SM Land Model (#ELM) is an integral part of the Energy Exascale Earth System Model (E3SM) framework, simulating the interactions between terrestrial land surfaces and other Earth system components. ELM has been instrumental in understanding hydrologic cycles, biogeophysics, and dynamics of terrestrial ecosystems. To facilitate the automatic porting of individual ELM modules, the researchers have developed a #software tool named SPEL. This work presents a SPEL extension that leverages the version control system (e.g., Git) to autonomous code generation and demonstrate its application to #continuous_code_integration and development of the ELM software system.  

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Aurora: a foundation model of the atmosphere "The implications of Aurora extend far beyond the realm of atmospheric forecasting. With its extensive potential applications in weather and climate sciences, Aurora represents a significant first step towards the development of a comprehensive foundation model encompassing the entire Earth system. Moreover, the ability of foundation models to excel at downstream tasks with scarce data could have profound implications for environmental forecasting in data-sparse regions, such as the developing world and the polar regions. By leveraging the knowledge learned from data-rich regions, foundation models could enable accurate forecasts even in areas where observational data is limited, thereby democratizing access to life-saving weather and climate information. This could have far-reaching impacts on sectors such as agriculture, transportation, energy harvesting, and disaster preparedness, enabling communities to better adapt to the challenges posed by climate change."

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🤔 What is a perceptron, and how does it work? --- Get our FREE data science cheat sheets in high resolution by subscribing to our blog today! https://lnkd.in/ejCdvUUA #machinelearning

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Language models are great compressors and can provide much better compression ratios than state-of-the-art compression algorithms. Some great insights here.

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Quite a nice overview, focusing on summarising key aspects of responsible foundational model development into a cheatsheet. My favorite part is Section 8, where they touch on the critical role of model evaluation, outline approaches to mitigating risks, and highlight some key shifts: 1) shift away from evaluating models, and towards evaluating systems; 2) shift away from evaluating on static toxicity/safety benchmarks, and towards evaluating on real-world, dynamically changing naturalistic observations, as well as human-interaction studies; 3) shift away from reporting evaluations, to releasing reproducible evaluation scripts. https://lnkd.in/gKHMvkAP

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