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Scaling Laws, Carefully: How to Fit and Interpret AI Models

#Scaling Laws#Deep Learning#Chinchilla#Kaplan#AI#GPT

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Controlling Reasoning Effort in LLMs: Does More Compute Mean Better Output?

#Reasoning Effort#LLM#Inference#Deep Learning#AI

Deep Neural Nets: 33 Years of Progress in 90 Seconds

#Deep Learning#Yann LeCun#MNIST#Model Reproducibility#AI History

Understanding Generative AI from the Ground Up with microgpt

#Generative AI#GPT#Autograd#LLM#Deep Learning

Scaling Laws, Carefully: Why the Foundation of AI Training Is Filled with Pitfalls

#Scaling Laws#Deep Learning#Chinchilla#AI Training#Data Efficiency

Scaling Laws, Carefully

#Scaling Laws#Deep Learning#Chinchilla#Kaplan#AI Training

Technologyarticle

Building Footprints from Aerial Imagery: A Practical GeoAI Pipeline

#GeoAI#Computer Vision#U-Net#NAIP Imagery#Deep Learning

Deep Neural Nets: 33 Years Ago and 33 Years From Now

#Deep Learning#Yann LeCun#MNIST#Neural Networks#AI History#Foundation Models

MicroGPT: 200 Lines of Pure Python That Train a GPT

#MicroGPT#Andrej Karpathy#LLM#Python#GPT-2#Deep Learning

Scaling Laws, Carefully: How Data Repetition and Fitting Choices Shape AI Performance

#Scaling Laws#Deep Learning#Chinchilla#Data Repetition#LLM Training

Scaling Laws, Carefully

#Scaling Laws#Deep Learning#Compute Allocation#Model Training

Scaling Laws Explain How Modern AI Models Grow Smarter

#Scaling Laws#Large Language Models#Chinchilla#Deep Learning#AI Training