machine learning – Page 20 – Andrew Fairless, Ph.D.

What I Read: Reinforcement Learning from Human Feedback

By Andrew Fairless on June 27, 2023May 31, 2023

https://huyenchip.com//2023/05/02/rlhf.html RLHF: Reinforcement Learning from Human FeedbackChip HuyenMay 2, 2023 “…making models like ChatGPT work. One such cool idea is RLHF (Reinforcement Learning from Human Feedback)…. So, how exactly doesContinue readingWhat I Read: Reinforcement Learning from Human Feedback

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: Computation, Artificial Intelligence

By Andrew Fairless on June 20, 2023May 1, 2023

https://www.quantamagazine.org/a-new-approach-to-computation-reimagines-artificial-intelligence-20230413/ A New Approach to Computation Reimagines Artificial IntelligenceAnil AnanthaswamyApril 13, 2023 “By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficientlyContinue readingWhat I Read: Computation, Artificial Intelligence

What I Read: Open Source, AlphaTensor

By Andrew Fairless on June 13, 2023May 1, 2023

https://www.kdnuggets.com/2023/03/first-open-source-implementation-deepmind-alphatensor.html First Open Source Implementation of DeepMind’s AlphaTensorDiego Fiori, Co-founder & CTO at NebulyMarch 10, 2023 “The first open-source implementation of AlphaTensor has been released and opens the door forContinue readingWhat I Read: Open Source, AlphaTensor

What I Read: Multi-label NLP

By Andrew Fairless on June 12, 2023May 1, 2023

https://www.kdnuggets.com/2023/03/multilabel-nlp-analysis-class-imbalance-loss-function-approaches.html Multi-label NLP: An Analysis of Class Imbalance and Loss Function ApproachesOleksii Babych, Machine Learning Engineer at ProvectusMarch 17, 2023 “Multi-label NLP refers to the task of assigning multiple labelsContinue readingWhat I Read: Multi-label NLP

What I Read: Unsupervised Learning Metrics

By Andrew Fairless on June 8, 2023May 1, 2023

https://www.kdnuggets.com/2023/04/exploring-unsupervised-learning-metrics.html Exploring Unsupervised Learning MetricsCornellius Yudha WijayaApril 13, 2023 “This article will discuss the metrics used to evaluate unsupervised machine learning algorithms…”

What I Read: One Large Model

By Andrew Fairless on June 5, 2023May 1, 2023

https://maithraraghu.com/blog/2023/does-one-model-rule-them-all/ Does One Large Model Rule Them All?Predictions on the Future AI EcosystemMaithra Raghu (Samaya AI), Matei Zaharia (Databricks), Eric Schmidt (Schmidt Futures)April 4, 2023 “Will the future AI landscapeContinue readingWhat I Read: One Large Model

What I Read: smaller LLMs, more tokens

By Andrew Fairless on May 31, 2023May 1, 2023

https://www.harmdevries.com/post/model-size-vs-compute-overhead/ Go smol or go homeWhy we should train smaller LLMs on more tokensHarm de VriesApr 13, 2023 “However, for most use cases you should not train a compute-optimal LLMContinue readingWhat I Read: smaller LLMs, more tokens

What I Read: Few Shot, Recommenders, LLMs

By Andrew Fairless on May 30, 2023May 1, 2023

https://blog.reachsumit.com/posts/2023/04/llm-for-recsys/ Zero and Few Shot Recommender Systems based on Large Language ModelsSumit Kumar2023-04-10 “Many researchers have recently proposed different approaches to building recommender systems using LLMs. These methods convert differentContinue readingWhat I Read: Few Shot, Recommenders, LLMs

What I Read: LLM applications, production

By Andrew Fairless on May 29, 2023May 1, 2023

https://huyenchip.com//2023/04/11/llm-engineering.html Building LLM applications for productionChip HuyenApr 11, 2023 “It’s easy to make something cool with LLMs, but very hard to make something production-ready with them.”

Tag: machine learning