In mathematics, the ADE classification (originally A-D-E classifications) is a situation where certain kinds of objects are in correspondence with simply laced Dynkin diagrams. The question of giving a common origin to these classifications, rather than a posteriori verification of a parallelism, was posed in. The complete list of simply laced Dynkin diagrams comprises Here "simply laced" means that there are no multiple edges, which corresponds to all simple roots in the root system forming angles of (no edge between the vertices) or (single edge between the vertices). These are two of the four families of Dynkin diagrams (omitting and ), and three of the five exceptional Dynkin diagrams (omitting and ). This list is non-redundant if one takes for If one extends the families to include redundant terms, one obtains the exceptional isomorphisms and corresponding isomorphisms of classified objects. The A, D, E nomenclature also yields the simply laced finite Coxeter groups, by the same diagrams: in this case the Dynkin diagrams exactly coincide with the Coxeter diagrams, as there are no multiple edges. (Wikipedia).

The BuShou of HanZi :舌

A brief description of the BuShou of 舌.

From playlist The BuShou of HanZi

The BuShou of HanZi :片

A brief description of the BuShou of 片.

From playlist The BuShou of HanZi

The BuShou of HanZi :目

A brief description of the BuShou of 目.

From playlist The BuShou of HanZi

The BuShou of HanZi : 門

A brief description of the BuShou of 門.

From playlist The BuShou of HanZi

The BuShou of HanZi :彳

A brief description of the BuShou of 彳.

From playlist The BuShou of HanZi

The BuShou of HanZi :禾

A brief description of the BuShou of 禾.

From playlist The BuShou of HanZi

The BuShou of HanZi :田

A brief description of the BuShou of 田.

From playlist The BuShou of HanZi

The BuShou of HanZi :耳

A brief description of the BuShou of 耳.

From playlist The BuShou of HanZi

SetFit and SBERT: ZERO Shot Classification w/ synthetic Data Set added (SBERT 47)

SetFit (trained on SBERT) was designed for few-shot learning, but the method can also be applied in scenarios where no (or not enough) labeled data is available for ZERO-Shot classification w/ synthetic data set added. The main trick is to create synthetic examples that resemble the clas

Stanford CS330 I Advanced Meta-Learning TopicsTask Construction l 2022 I Lecture 9

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu​ Chelsea Finn Computer

Few-Shot Text Classification in the Real-World

Intel Lab SPE Moshe Wasserblat will review SoTA methods for few-shot learning in the real-world and recent benchmarks.

From playlist Healthcare NLP Summit 2022

Support Vector Machines (SVMs): A friendly introduction

Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt An introduction to support vector machines (SVMs) that requires very little math (no calculus or linear algebra), only a visual mind. This is the third of a series of three vi

From playlist General Machine Learning

Keras Image Classification Tutorial | Image Classification Using Deep Learning | Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=KerasImageClassification-LKMi8Daf2ts&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: ht

Is it Enough to Simply Apply Language Model for Optimal Text Classification?

Presented by: Meysam Ghaffari - Senior Data Scientist (NLP and Deep Learning) at Memorial Sloan Kettering Cancer Center Using language models to solve the NLP tasks is getting more popular each day. It has been proven that language models can give us state of the art results in most of th

From playlist Healthcare NLP Summit 2022

Lecture 0301 Classification

Machine Learning by Andrew Ng [Coursera] 03-01 Logistic Regression

From playlist Machine Learning by Professor Andrew Ng

Accelerating Clinical Data Abstraction and Real-World Data Curation with Active Learning

Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/​ Watch all Healthcare NLP Summit 2021 sessions: https://www.nlpsummit.org/​ Building large-scale structured datasets o

From playlist Healthcare NLP Summit 2021

The BuShou of HanZi : 車

A brief description of the BuShou of 車.

From playlist The BuShou of HanZi

New TensorFlow Features for 🤗 Transformers and 🤗 Datasets

Matt shows us how easy it is to use the Datasets library and Keras.fit to fine-tune a Transformer model with TensorFlow. Matt is responsible for TensorFlow maintenance at Transformers, and will eventually lead a coup against the incumbent PyTorch faction which will likely be co-ordinated

From playlist Hugging Face Course Event