supervised – Andrew Fairless, Ph.D.

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: High-Quality Human Data

By Andrew Fairless on April 17, 2024March 1, 2024

https://lilianweng.github.io/posts/2024-02-05-human-data-quality/Thinking about High-Quality Human DataLilian WengFebruary 5, 2024 “High-quality data is the fuel for modern data deep learning model training.”

What I Read: LLM Training, RLHF

By Andrew Fairless on October 30, 2023October 5, 2023

https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives LLM Training: RLHF and Its AlternativesSebastian Raschka, PhDSep 10, 2023 “RLHF is an integral part of the modern LLM training pipeline due to its ability to incorporate human preferencesContinue readingWhat I Read: LLM Training, RLHF

What I Read: AIs producing own training data

By Andrew Fairless on July 10, 2023May 31, 2023

https://thegradient.pub/software2-a-new-generation-of-ais-that-become-increasingly-general-by-producing-their-own-training-data/ Software²: A new generation of AIs that become increasingly general by producing their own training dataMinqi Jiang22.Apr.2023 “We are at the cusp of transitioning from “learning from data” toContinue readingWhat I Read: AIs producing own training data

What I Read: Against LLM

By Andrew Fairless on June 29, 2023May 31, 2023

https://explosion.ai/blog/against-llm-maximalism Against LLM maximalismby Matthew HonnibalMay 18, 2023 “However, LLMs are not a direct solution to most of the NLP use-cases companies have been working on… if you want toContinue readingWhat I Read: Against LLM

What I Read: Reinforcement Learning, Language Models

By Andrew Fairless on June 21, 2023May 1, 2023

https://gist.github.com/yoavg/6bff0fecd65950898eba1bb321cfbd81 Reinforcement Learning for Language ModelsYoav Goldberg, April 2023. “With the release of the ChatGPT model… there was a lot of discussion of the importance of “RLHF training”, that is,Continue readingWhat I Read: Reinforcement Learning, Language Models

What I Read: Text Embeddings Visually Explained

By Andrew Fairless on July 26, 2022July 8, 2022

https://txt.cohere.ai/text-embeddings/ Text Embeddings Visually ExplainedMeor AmerJun 28, 2022 “We take a visual approach to gain an intuition behind text embeddings, what use cases they are good for, and how theyContinue readingWhat I Read: Text Embeddings Visually Explained

What I Read: Generalization of SGD

By Andrew Fairless on May 10, 2022April 25, 2022

https://www.quantamagazine.org/machine-learning-reimagines-the-building-blocks-of-computing-20220315/ Machine Learning Reimagines the Building Blocks of ComputingNick ThiemeContributing WriterMarch 15, 2022 “Traditional algorithms power complicated computational tools like machine learning. A new approach, called algorithms with predictions, usesContinue readingWhat I Read: Generalization of SGD

What I Read: Reinforcement learning is supervised learning

By Andrew Fairless on February 2, 2021January 8, 2021

https://bair.berkeley.edu/blog/2020/10/13/supervised-rl/ Reinforcement learning is supervised learning on optimized dataBen Eysenbach and Aviral Kumar and Abhishek Gupta Oct 13, 2020 “This suggests that we can simply use inverse RL to relabelContinue readingWhat I Read: Reinforcement learning is supervised learning

What I Read: Production with Deep Semi-Supervised Learning

By Andrew Fairless on January 28, 2021January 2, 2021

https://towardsdatascience.com/from-research-to-production-with-deep-semi-supervised-learning-7caaedc39093 From Research to Production with Deep Semi-Supervised LearningVarun NairSep 25 “Semi-supervised learning (SSL), a subfield that combines both supervised and unsupervised learning, has grown in popularity in the deepContinue readingWhat I Read: Production with Deep Semi-Supervised Learning

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