Andrew Fairless – Page 58 – Andrew Fairless, Ph.D.

What I Read: Languages, number of terms for colors

By Andrew Fairless on July 22, 2021July 12, 2021

https://theconversation.com/languages-dont-all-have-the-same-number-of-terms-for-colors-scientists-have-a-new-theory-why-84117 Languages don’t all have the same number of terms for colors – scientists have a new theory whySeptember 18, 2017 “People with standard vision can see millions of distinctContinue readingWhat I Read: Languages, number of terms for colors

What I Read: Double Machine Learning for causal inference

By Andrew Fairless on July 21, 2021July 2, 2021

https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Double Machine Learning for causal inferenceHow Double Machine Learning for causal inference works, from the theoretical foundations to an example of application.Borja VelascoJun 25 “This post tries to explain,Continue readingWhat I Read: Double Machine Learning for causal inference

What I Read: Semantic Search

By Andrew Fairless on July 20, 2021July 2, 2021

https://www.pinecone.io/learn/semantic-search/ Semantic Search: Measuring Meaning From Jaccard to BertJames BriggsData Scientist “Similarity search is a complex topic and there are countless techniques for building effective search engines. In this article,Continue readingWhat I Read: Semantic Search

What I Read: Same or Different? The Question Flummoxes Neural Networks

By Andrew Fairless on July 19, 2021July 2, 2021

https://www.quantamagazine.org/same-or-different-ai-cant-tell-20210623/ Same or Different? The Question Flummoxes Neural Networks.John PavlusContributing WriterJune 23, 2021 “One of the most powerful classes of artificial intelligence systems, known as convolutional neural networks or CNNs…Continue readingWhat I Read: Same or Different? The Question Flummoxes Neural Networks

What I Read: Do Multi-Task Learning Intelligently

By Andrew Fairless on July 15, 2021July 2, 2021

https://thegradient.pub/how-to-do-multi-task-learning-intelligently/ How to Do Multi-Task Learning Intelligently22.Jun.2021 “Traditionally, a single machine learning model is devoted to one task… There are some advantages, however, to training models to make multiple kindsContinue readingWhat I Read: Do Multi-Task Learning Intelligently

What I Read: Tabular Data, Deep Learning is Not All You Need

By Andrew Fairless on July 14, 2021June 22, 2021

https://arxiv.org/abs/2106.03253v1 Tabular Data: Deep Learning is Not All You NeedRavid Shwartz-Ziv, Amitai Armon “Our study shows that XGBoost outperforms these deep models across the datasets, including datasets used in theContinue readingWhat I Read: Tabular Data, Deep Learning is Not All You Need

What I Read: How troubleshoot memory problems in Python

By Andrew Fairless on July 13, 2021June 22, 2021

https://innovation.alteryx.com/how-to-troubleshoot-memory-problems-in-python/ How to troubleshoot memory problems in PythonFreddy BoultonJun 16, 2021 “Memory problems are hard to diagnose and fix in general, but I’d argue it’s even harder in Python. Python’sContinue readingWhat I Read: How troubleshoot memory problems in Python

What I Read: Dask vs Vaex

By Andrew Fairless on July 12, 2021June 22, 2021

https://vaex.io/blog/dask-vs-vaex-a-qualitative-comparison Dask vs Vaex – a qualitative comparisonJovan VeljanoskiJune 15, 2021 “Vaex is flat out the fastest and most memory efficient Python DataFrame library out there…. There are still plentyContinue readingWhat I Read: Dask vs Vaex

What I Read: null hypothesis significance testing falls apart when considering replications

By Andrew Fairless on July 8, 2021June 22, 2021

https://statmodeling.stat.columbia.edu/2021/06/17/guttman-points-out-another-problem-with-null-hypothesis-significance-testing-it-falls-apart-when-considering-replications/ Guttman points out another problem with null hypothesis significance testing: It falls apart when considering replications.by Andrew Gelman17 June 2021, 9:22 am “… another problem with null hypothesis significanceContinue readingWhat I Read: null hypothesis significance testing falls apart when considering replications

What I Read: Five types of thinking

By Andrew Fairless on July 7, 2021June 22, 2021

https://towardsdatascience.com/five-types-of-thinking-for-a-high-performing-data-scientist-8ab70d70c23b?gi=772956cb941c Thinking about thinking (Part 1)Five types of thinking for a high performing data scientistFrom mental models to computational thinkingAnand S RaoApr 25 “As data scientists, we have to understandContinue readingWhat I Read: Five types of thinking

Author: Andrew Fairless