A modern approach to Grokking System Design. One course to master distributed systems and scalable architecture patterns. Practice with mock interviews.
Grokking can be understood as a phase transition during the training process. [5] In particular, recent work has shown that grokking may be due to a complexity phase transition in the model during training. [6] While grokking has been thought of as largely a phenomenon of relatively shallow models, grokking has been observed in deep neural networks and non-neural models and is the subject of ...
In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, generalization, and speed of learning can be studied in great detail. In some situations we show that neural networks learn through a process of "grokking" a pattern in the data, improving generalization performance ...
Emergent Abilities and Grokking: As Huang et al. argue, emergent abilities in very large models might be essentially grokking happening along the scale axis arxiv.org. This raises an exciting prospect: by studying grokking in controlled settings, can we predict what abilities will emerge in frontier models and at what point?
Abstract This paper reviews the phenomenon of “grokking” in neural networks, where models initially overfit their training data but later experience a sudden improvement in test performance after prolonged training. This behaviour defies traditional expectations of the bias-variance trade-of and challenges our understanding of neural network generalisation. We explore the characteristics ...
Explore the phenomenon of grokking in deep learning. Learn how Ultralytics YOLO26 models transition from memorization to generalization during extended training.