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Table of Contents
1. Introduction
2. On the difficult optimization problem of training deep neural networks
3. A curriculum as a continuation method
4. Toy Experiments with a Convex Criterion
5. Experiments on shape recognition
Summary
Humans and animals learn much better when examples are organized in a meaningful order called curriculum learning. This paper formalizes training strategies in machine learning using curriculum learning. The experiments demonstrate significant improvements in generalization. The paper discusses the impact of curriculum learning on training convergence and quality of local minima. It also explores the concept of a curriculum as a continuation method for global optimization. The experiments include toy experiments with a convex criterion and shape recognition experiments using neural networks. The results show that curriculum learning can lead to faster convergence and improved generalization in machine learning tasks.