Feature scaling is a cornerstone of effective machine learning, addressing challenges posed by datasets with features on different scales. In my latest blog, I explore: ✅ The critical role of feature scaling in improving model accuracy and fairness. ✅ Advanced techniques like Min-Max Scaling and Standardization. ✅ Strategic considerations for selecting the optimal approach based on the dataset. Dive in to learn how scaling can elevate your preprocessing pipeline and drive better outcomes. I look forward to your perspectives and insights.
Meenakshi Reghu’s Post
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Incident Management | Digital Forensics | Generative AI Security Architect | Threat Intelligence | Threat Hunting | Security Assessments
Understanding the complexities of machine learning compilers is essential for anyone involved in the field. In my latest blog post, I delve into the reasons behind these challenges, exploring how they impact performance, efficiency, and deployment. This insightful discussion sheds light on the intricacies of ML compilers and what makes them difficult to optimize. Join the conversation and enhance your understanding of this crucial topic by reading more at the following link: https://ift.tt/z6PDcxO.
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Fresh, insightful and accessible look by Google DeepMind into Diffusion vs. Gaussian Flow Matching. They’re actually interchangeable methods for training and sampling. e.g once a flow matching model is trained, can opt for either stochastic or deterministic sampling. Blog: https://lnkd.in/gHCYNNJT Image by Ruiqi Gao.
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After setting the stage with the basic concepts of outlier detection, Sara Nóbrega's series turns to hands-on implementation, introducing us to several effective ML methods and tools for outlier detection in the context of time-series analysis.
The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 2)
towardsdatascience.com
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Adjunct, Associate Professor of Infectious Diseases, School of Medicine. The University of Jordan.
Any idea; On defining a breakpoint for the difference between two reference groups, which is more precise and accurate in definning a new value classification. Receiver operator characteristic curve (ROCC) OR Support Vector Machine (SVM) in machine learning. Thanks for the enlightens.
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Curious about using machine learning in trading strategies? Check out our new, free tutorial for MoonshotML, QuantRocket’s machine learning backtester. Learn how to use walk-forward optimization with a random forest classifier to backtest a machine learning strategy that uses past returns to predict future returns. View the tutorial here: https://lnkd.in/eDqjh8Gx
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🚀 Are forecasting models the future? In this blog post, we delve deep! As foundation models and forecasting gain significance in the ever-changing technological landscape, many questions arise. Are we on the brink of a forecasting revolution? How do these innovative models perform in real-world scenarios? And can modern foundation models outpace the trusted traditional algorithms in accuracy? 🤔 You’ll find out in this blog post, in which Adriaan Van Haecke, Machine Learning Engineer at Radix, investigates the transformative potential of foundation models and forecasting. Curious about the main takeaways? We’ve listed them for you, swipe to learn more! Interested in reading the full blog post? Check it out via the link in the comments! 👇
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Advancing time series analysis with multi-granularity guided diffusion model; An algorithm-system co-design for fast, scalable MoE inference; What makes a search metric successful in large-scale settings; learning to solve PDEs without simulated data. https://msft.it/6045lyVfD
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