classification – Page 2 – Andrew Fairless, Ph.D.

What I Read: Few-Shot Learning

By Andrew Fairless on September 12, 2023September 4, 2023

https://saturncloud.io/blog/breaking-the-data-barrier-how-zero-shot-one-shot-and-few-shot-learning-are-transforming-machine-learning/ Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning are Transforming Machine LearningBy Christophe AttenWednesday, May 17, 2023 “The improvements made over the last almost two decades haveContinue readingWhat I Read: Few-Shot Learning

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: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

What I Read: Estimating Model Performance

By Andrew Fairless on August 23, 2022July 18, 2022

https://pub.towardsai.net/estimating-model-performance-without-ground-truth-453b850dad9a Estimating Model Performance without Ground TruthIt’s possible, as long as you keep your probabilities calibratedMichał OleszakMay 12 “A model in production is like a baby: it needs to beContinue readingWhat I Read: Estimating Model Performance

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: binary cross-entropy, log loss

By Andrew Fairless on October 25, 2021October 16, 2021

https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a?gi=375ce73be21b Understanding binary cross-entropy / log loss: a visual explanationDaniel GodoyNov 21, 2018 “If you are training a binary classifier, chances are you are using binary cross-entropy / log lossContinue readingWhat I Read: binary cross-entropy, log loss

What I Read: Machine learning is not nonparametric statistics

By Andrew Fairless on October 20, 2021October 16, 2021

https://www.argmin.net/2021/10/13/prediction/ Machine learning is not nonparametric statistics.Ben RechtOct 13, 2021 “…I’ve been told by respected statisticians that machine learning is nothing more than nonparametric statistics. The longer I work inContinue readingWhat I Read: Machine learning is not nonparametric statistics

What I Read: Deep One-class Classification

By Andrew Fairless on October 4, 2021September 21, 2021

https://arxiv.org/abs/2011.02578 Learning and Evaluating Representations for Deep One-class ClassificationKihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister “We present a two-stage framework for deep one-class classification. We first learnContinue readingWhat I Read: Deep One-class Classification

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

What I Read: Parallelizing neural networks, GPU, JAX

By Andrew Fairless on July 28, 2021July 12, 2021

https://willwhitney.com/parallel-training-jax.html Will WhitneyParallelizing neural networks on one GPU with JAX How you can get a 100x speedup for training small neural networks by making the most of your accelerator.“Most neuralContinue readingWhat I Read: Parallelizing neural networks, GPU, JAX

Tag: classification