Curriculum Learning is a training strategy in machine learning where models are trained on increasingly complex tasks. It mimics human learning by starting with more straightforward concepts and gradually increasing difficulty.
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Imagine when you're teaching a child to read. If you begin with complex things, they may never learn quickly. So you would begin with the alphabet, then simple words, and gradually move on to more complex sentences and stories. That's the basic idea behind curriculum learning in AI.
Instead of overwhelming an AI model with tons of complex data all at once, we feed it information in a structured way. Then, start with easy examples the model can quickly grasp and slowly increase the difficulty. This helps the model learn more effectively and efficiently.
First, Easy examples: Simple addition problems like 1+1 or recognizing basic shapes like circles and squares.
Harder examples: Complex calculus problems or identifying objects in a cluttered environment.
By starting with the easy stuff, the model builds a strong foundation.
Types:
Task-Based Curriculum: Training on tasks of increasing complexity.
Difficulty-Based Curriculum: Gradually increase the difficulty of tasks.
Instance-Based Curriculum: Ordering training instances from easy to hard.
Uses:
- Natural Language Processing (NLP): Improving language models.
- Reinforcement Learning: Training agents to perform complex tasks.
- Computer Vision: Enhancing image recognition.
Examples:
In Language Models: We start with simple sentences and then progress to complex paragraphs.
Robotics: The beginning often begins with simple movements and then advances to complex actions.
Image Classification: Start with the clearest images, then move to more challenging ones.
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