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Taking Pandas To The Next Level With LLMs

Mlearning.ai

describe() count 9994 mean 2017-04-30 05:17:08.056834048 min 2015-01-03 00:00:00 25% 2016-05-23 00:00:00 50% 2017-06-26 00:00:00 75% 2018-05-14 00:00:00 max 2018-12-30 00:00:00 Name: Order Date, dtype: object Average sales per year df['year'] = df['Order Date'].apply(lambda Yearly average sales.

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One-way ANOVA for Non-normal and Non-homogeneous Data with Box-Cox Transformation in R

Universe of Data Science

In this part, we will use AADT dataset available in AID package (Dag and Ilk, 2017). How to Apply Box-Cox Transformation for One-way ANOVA in R We use boxcoxfr() function available in AID package (Dag and Ilk, 2017) to apply Box-Cox transformation for one-way ANOVA in R. Then, we will conduct one-way ANOVA and pairwise comparison.

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NLP News Cypher | 09.13.20

Towards AI

declassified Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. It leverages an interface across tasks that are grounded on a single knowledge source: the 2019/08/01 Wikipedia snapshot containing 5.9M

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NLP News Cypher | 09.13.20

Towards AI

link] Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. It leverages an interface across tasks that are grounded on a single knowledge source: the 2019/08/01 Wikipedia snapshot containing 5.9M

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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.

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Discovering an Antimalarial Drug in Mao’s China

Hacker News

Wendi Yan writes about the discovery of artemisinin, a medicine that has saved millions of lives, for Issue 01. Drawing on recently published Chinese texts, Wendi’s reporting provides a more comprehensive telling of this discovery than has previously been described.

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Training the YOLOv8 Object Detector for OAK-D

PyImageSearch

Redmon and Farhadi (2017) published YOLOv2 at the CVPR Conference and improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters. YOLO, or YOLOv1, was the first single-stage object detection model. It quickly gained popularity due to its high speed and accuracy. The authors continued from there.