Reference 9 stops to get here

Random Crop

Extracting random patches from images for augmentation and training.

Your route here

9 stops · basics first

Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
Overfitting ✓ understood
When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.
Data Augmentation ✓ understood
Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization.
Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
Deep Learning ✓ understood
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
Computer Vision ✓ understood
The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.
Image Augmentation ✓ understood
Applying transformations (rotation, flip, crop, color) to increase training data diversity.
Random Crop · you are here ✓ understood

Extracting random patches from images for augmentation and training.

This concept is essential for understanding computer vision and forms a key part of modern AI systems.

Before this

Random Crop

Leads to

Nothing yet: a destination in its own right.