Andrew Fairless – Page 4 – Andrew Fairless, Ph.D.

What I Read: string ufuncs, NumPy 2.0

By Andrew Fairless on August 27, 2024June 15, 2024

https://labs.quansight.org/blog/numpy-string-ufuncs Writing fast string ufuncs for NumPy 2.0Lysandros NikolaouPublished May 21, 2024 “After a huge amount of work from many people, NumPy 2.0.0 will soon be released, the first NumPyContinue readingWhat I Read: string ufuncs, NumPy 2.0

What I Read: neural systems understanding

By Andrew Fairless on August 26, 2024June 15, 2024

Can an emerging field called ‘neural systems understanding’ explain the brain? Can an emerging field called ‘neural systems understanding’ explain the brain?George Musser5 June 2024 “This mashup of neuroscience, artificialContinue readingWhat I Read: neural systems understanding

What I Read: Cookiecutter Data Science V2

By Andrew Fairless on August 22, 2024June 4, 2024

https://drivendata.co/blog/ccds-v2 Cookiecutter Data Science V2by Peter Bull, Jay Qi, Chris KucharczykWed 22 May 2024 “…in the past 5 years, a lot has changed in data science tooling and MLOps. CookiecutterContinue readingWhat I Read: Cookiecutter Data Science V2

What I Read: KL All You Need

By Andrew Fairless on August 21, 2024June 4, 2024

https://blog.alexalemi.com/kl-is-all-you-need.html KL is All You NeedAlexander A. Alemi2024-01-08 “…the core of essentially all modern machine learning methods is a single universal objective: Kullback-Leibler (KL) divergence minimization…. Understand KL, understand theContinue readingWhat I Read: KL All You Need

What I Read: What We Learned Building LLMs

By Andrew Fairless on August 19, 2024June 4, 2024

https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i What We Learned from a Year of Building with LLMs (Part I)By Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu and Shreya ShankarMay 28, 2024 “We’ve identifiedContinue readingWhat I Read: What We Learned Building LLMs

What I Read: Merge Large Language Models

By Andrew Fairless on August 15, 2024June 4, 2024

https://huggingface.co/blog/mlabonne/merge-models Merge Large Language Models with mergekitJanuary 9, 2024Maxime Labonne “Model merging is a technique that combines two or more LLMs into a single model. It’s a relatively new andContinue readingWhat I Read: Merge Large Language Models

What I Read: Transformers by Hand

By Andrew Fairless on August 14, 2024May 25, 2024

https://towardsdatascience.com/deep-dive-into-transformers-by-hand-%EF%B8%8E-68b8be4bd813?gi=b2b3c1885179 Deep Dive into Transformers by HandSrijanie Dey, PhDApr 12, 2024 “…the two mechanisms that are truly the force behind the transformers are attention weighting and feed-forward networks (FFN).”

What I Read: LLM Pipelines, DSPy

By Andrew Fairless on August 13, 2024May 25, 2024

https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy Optimizing Databricks LLM Pipelines with DSPyby Arnav Singhvi and Daniel Pechi (JetBlue)May 23, 2024 “The key component of DSPy is self-improving pipelines…. Just as data pipelines and machine learningContinue readingWhat I Read: LLM Pipelines, DSPy

What I Read: LLM evaluation

By Andrew Fairless on August 7, 2024May 25, 2024

https://huggingface.co/blog/clefourrier/llm-evaluation Let’s talk about LLM evaluationMay 23, 2024Clémentine Fourrier “There are, to my knowledge, at the moment, 3 main ways to do evaluation: automated benchmarking, using humans as judges, andContinue readingWhat I Read: LLM evaluation

What I Read: LLM, DSPy Assertions and Suggestions

By Andrew Fairless on August 6, 2024May 25, 2024

https://learnbybuilding.ai/tutorials/guiding-llm-output-with-dspy-assertions-and-suggestions Guiding LLM Output with DSPy Assertions and SuggestionsBill Chambers “Assertions in DSPy allow you to define strict rules and constraints that the LLM’s output must (or maybe that youContinue readingWhat I Read: LLM, DSPy Assertions and Suggestions

Author: Andrew Fairless