Feedback loops are critical elements of real-world #machinelearning systems. They enable the model to improve our time, and help collect feedback on where the model is making mistakes
MACHINE LEARNING
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F1 Score Averaging Methods in Multi-Class Classification
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Micro, Macro & Weighted Averages of F1 Score, Clearly Explained: Understanding the concepts behind the micro average, macro average, and weighted average of F1 score in multi-class classification with simple illustrations. https://
kdnuggets.com/2023/01/micro-
macro-weighted-averages-f1-score-clearly-explained.html?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=micro-macro-weighted-averages-of-f1-score-clearly-explained
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Scaling Unlocks Emergent Abilities in Language Models Talk
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I gave a version of this talk "Scaling unlocks emergent abilities in language models" today at USC ISI. There is a video recording: https://
youtu.be/Z_Qt737HG-0 Thanks Justin Cho @HJCH0 for inviting me and organizing! -
Top 8 Machine Learning Algorithms to Study This Weekend
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RT Here's a list of top 8 ML algorithms that you must go through over the weekend: http://
hubs.la/Q01xvDSf0
by @datasciencedojo #MachineLearning #Algorithms #DataScience -
Topological Deep Learning Thesis Defense Recognition
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Congratulations Chris. It was a magnificent thesis and defense. I recommend your thesis to everyone who wants to learn about topological deep learning!
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Flan-T5 Outperforms OPT-IML with Greater Efficiency
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Check out OPT-IML, but a lot of people said they didn't know:
– Flan-T5 checkpoints are publicly available (without requesting access)
– Flan-T5 11B outperforms OPT-IML on MMLU and Big-Bench Hard, despite being 10x more compute efficient Checkpoints: https://
huggingface.co/docs/transform
ers/model_doc/flan-t5
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Dot Product and Cosine Similarity in NLP Explained
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In this post, @luis_likes_math defines two types of similarities for sentences: dot product similarity and cosine similarity. These similarities are very useful in determining if two sentences are similar or different. Learn more about similarity in NLP in this exciting post:
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Cerebras Enables Larger Image Semantic Segmentation Training
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Cerebras has made it possible to train and run inference on semantic segmentation models on images larger than can be done on GPUs today. pic.twitter.com/K938yfuFlF
— Cerebras (@cerebras) 3 février 2023Cerebras has made it possible to train and run inference on semantic segmentation models on images larger than can be done on GPUs today.
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ML and Computer Systems Intersection: Distributed Systems Focus
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Part 3 in the series is out, by Mangpo Phothilimthana and @apaszke
, covering the intersection of ML and computer systems (distributed systems, compilers, hardware design, energy-efficient and low emissions computing, etc). -
Balancing Precision and Recall in Machine Learning Models
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#WisdomWednesday! A good model balances: #Precision: proportion of emails correctly identified as spam out of all the emails labeled as spam [60%] +
#Recall: proportion of spam emails correctly identified out of all the spam emails in the dataset [75%] #machinelearning