Haha good one . Jensen-Shannon divergence: Training DNNs on Nvidia GPUs is not reproducible due to randomly selected code
MACHINE LEARNING
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GitHub Guide: Tips for Working with Large Language Models
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GitHub – brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4. https://bit.ly/46l3eoi #AI #MachineLearning #DeepLearning #LLMs #DataScience
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AI in Healthcare: Promising but Not Yet Ready for Patient Care
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AI may be on its way to your doctor’s office, but it’s not ready to see patients
#AI #AIio #BigData #ML #NLU #Futureofwork @IIoT_World @ipfconline1 @jblefevre60 @JimMarous @insurtechtalk @KirkDBorne @kuriharan @LouisSerge http://
ow.ly/mfJx30svStx -
AI in Healthcare: Promise and Current Limitations for Patient Care
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AI may be on its way to your doctor’s office, but it’s not ready to see patients
#AI #AIio #BigData #ML #NLU #Futureofwork http://
ow.ly/m5ps30svSfa -
Considering accuracy trade-offs and LoRA alternatives
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Thanks for suggesting! Yeah, I was thinking about that as well, but it may affect accuracy, and then once going this route, one might as well also think about LoRA etc.
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Fabric’s Intuitive API for PyTorch Script Modifications
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I can't say much about accelerate since I never really used it, but regarding Fabric, I like the pretty intuitive API. It's basically just a minimal number of lines of code to modify a PyTorch script.
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Vision Transformers: Tokenizing Images Like Language Models
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In a tweet: similar to LLMs (language transformers) but tokenizing images instead of text:
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MIT MAS.S68 Course on AI and Machine Learning
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MIT MAS.S68! https://
bit.ly/441rwBF #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Modern Classification: Multiple Output Nodes vs Sigmoid Threshold
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So, in this case, rather than using a threshold on a logistic sigmoid score [0, 1], in most modern architectures, you usually define at least 2 output nodes. Then, you determine the class based on which output node has the highest score.
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Memory-Saving Techniques for Tabular Data Transformers
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but yeah, I am training classifiers in both cases, the same memory-saving techniques could potentially be applied to the recent transformers for tabular data.