Gradient-weighted Class Activation Mapping - visualizing which image regions influenced CNN predictions.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
A mathematical operation that applies filters/kernels to input data to extract features like edges, textures, and patterns.
A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects.
Understanding the internal workings of AI models, including which features influence predictions and why.
A collection of data examples used for training, validating, or testing machine learning models.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.
The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.
A visualization highlighting input regions most important for model predictions.
Gradient-weighted Class Activation Mapping - visualizing which image regions influenced CNN predictions.
This concept is essential for understanding specialized ai topics and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.