4). Language Modeling on Tabular Data – presents a comprehensive survey of language modeling techniques for tabular data.
MACHINE LEARNING
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LLM Pruning and Distillation: Compressing Llama and Mistral Models
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2). LLM Pruning and Distillation in Practice – provides a comprehensive report on effective methods for compressing Llama 3.1 and Mistral NeMo models; it presents pruning and distillation approaches applied to the original models to produce 4B and 8B parameter models,
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Top ML Papers: GraphRAG, LLMs, Agents and Robustness
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The Top ML Papers of the Week (August 19 – 25): – GraphRAG Methods
– LLMs for Tabular Data
– Automated Agentic Systems
– Enhancing Robustness in LLMs
– Controllable Text Generation for LLMs
– LLM Pruning and Distillation in Practice
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Wrap-up: Python, Machine Learning, and AI Engineering Resources
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That's a wrap! If you interested in: – Python – Machine Learning – AI Engineering Find me → @akshay_pachaar Subscribe to my AI Engineering Newsletter: @ML_Spring Cheers!
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PR Curve: Evaluating and Comparing Machine Learning Models
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PR curve is also a great way to evaluate & compare different models. A model with perfect precision & recall will have a PR curve that hugs the top right corner of the graph. So, the area under the curve can also be used as a metric to compare different models. Check this
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Creating PR-Curve for Logistic Regression Model
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Let's take first our first example & create PR-curve for a logistic regression model! Check this out
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PR-Curve: Balancing Precision and Recall in Machine Learning
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Precision and recall are two competing metrics & PR-curve helps us strike a balance! PR-curve is a graphical representation that shows the trade-off between precision & recall for different threshold values. Here are 5 simple steps to create one!
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Creating a PR-Curve to Balance Precision and Recall
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A Data Scientist often needs to strike a balance between Precision & Recall & creating a PR-curve is the best way to do so. Let's understand how to create one today!
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Precision Recall PR Curve Clear Explanation Guide
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Precision Recall (PR) curve, clearly explained:
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ML Training Best Practices Reduce CO2e Emissions Effectively
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Measurement is important (rather than using flawed estimates). Even more important, though, is taking action to perform ML training using best practices to reduce or eliminate CO2e emissions. See https://
arxiv.org/abs/2104.10350