The Modern Data Toolbox: Combining LLMs, ML, and Statistics for Greater Impact
pub.towardsai.net·17h
🧩Neurodiverse
One More Machine Learning Article: When Computers Start to Think for Themselves
dev.to·3d·
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Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning
arxiv.org·1d
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Finding MLB Player Types with K-Means Clustering
runningonnumbers.com·20h·
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Looking for feature absorption automatically
lesswrong.com·14h
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Classifier Language Models: Unifying Sparse Finetuning and Adaptive Tokenization for Specialized Classification Tasks
arxiv.org·7h
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How to implement Random Forests in R
dev.to·17h·
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🦀Rust
The best explanation of LLMs I've ever seen is in this new book.
threadreaderapp.com·1d
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Comparative study of machine learning and statistical methods for automatic identification and quantification in {\gamma}-ray spectrometry
arxiv.org·7h
🦀Rust
Wrote a Beginner-Friendly Linear Regression Tutorial (with Full Code)
medium.com·2d
🦀Rust
Learning to Select MCP Algorithms: From Traditional ML to Dual-Channel GAT-MLP
arxiv.org·1d
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Few-Shot Anomaly Detection in Manufacturing via Bayesian Hypernetwork Optimization
dev.to·1d·
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How to Read AI Results (Even If You're Not an Expert)
hackernoon.com·5h
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AI Frontiers: Advances in Efficient, Robust, and Universal Machine Learning – Synthesizing Key Themes from August 2025 a
dev.to·1d·
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Subsampling Factorization Machine Annealing
arxiv.org·7h
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Estimating from No Data: Deriving a Continuous Score from Categories
towardsdatascience.com·1d
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MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder
arxiv.org·7h
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How I Explained Vector Embeddings to My Mom (And She Got It)
dev.to·8m·
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Writing an LLM from scratch, part 17 – the feed-forward network
gilesthomas.com·13h·
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Meet the early-adopter judges using AI
bespacific.com·9h
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