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Tune In: Decision Threshold Optimization with scikit-learn’s TunedThresholdClassifierCV
Tune In: Decision Threshold Optimization with scikit-learn’s TunedThresholdClassifierCV
Use cases and code to explore the new class that helps tune decision thresholds in scikit-learn.
Kevin Arvai
May 27
Physics-Informed Neural Network with Forcing Function
Physics-Informed Neural Network with Forcing Function
with code
John Morrow
May 27
Latest
Quantize Llama 3 8B with Bitsandbytes to Preserve Its Accuracy
Quantize Llama 3 8B with Bitsandbytes to Preserve Its Accuracy
Llama 2 vs. Llama 3 vs. Mistral 7B, quantized with GPTQ and Bitsandbytes
Benjamin Marie
May 27
Intuitive Temporal DataFrame Filtration
Intuitive Temporal DataFrame Filtration
Get rid of your ugly code for filtering timeseries data
Yousef Nami
May 27
Implement a Star Schema for Power BI Semantic Model: Step-by-Step Guide
Implement a Star Schema for Power BI Semantic Model: Step-by-Step Guide
Star schema is a well-known concept in dimensional modeling. In this article, you’ll learn how to implement it by using Power Query
Nikola Ilic
May 27
Dask DataFrame is Fast Now
Dask DataFrame is Fast Now
Patrick Hoefler
May 27
Passing Functions to Test Files in Python Pytest
Passing Functions to Test Files in Python Pytest
This is a very frequent question, but the solution is very simple: use a fixture.
Marcin Kozak
May 27
Why Representation Finetuning is the Most Efficient Approach Today?
Why Representation Finetuning is the Most Efficient Approach Today?
A Step-by-Step Guide to Representation Finetuning LLAMA3
Yanli Liu
May 26
How to Reduce Embedding Size and Increase RAG Retrieval Speed
How to Reduce Embedding Size and Increase RAG Retrieval Speed
Flexible text embedding with Matryoshka Representation Learning (MRL)
Dr. Leon Eversberg
May 26
Difference-in-Difference 101
Difference-in-Difference 101
What is Difference-in-difference (DiD or DD or diff-in-diff)? Why do we care about DiD?
Henam Singla
May 26
The Past, Present, and Future of Data Quality Management: Understanding Testing, Monitoring, and…
The Past, Present, and Future of Data Quality Management: Understanding Testing, Monitoring, and…
The data estate is evolving, and data quality management needs to evolve with it.
Barr Moses
May 25
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