Transfer learning: 'storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks'
Reinforcement learning:
- 'focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).'
- No need of labelled data or explicit correction. Sort of learns by itself based on what it sees.
- 'concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward'
ReLu: Rectified linear unit
- A popular activation function in ANNs
f(x) = max(0, x)
Meta-learning: 'Learning to learn'
Support vector machine (SVM)
- For (linear) classification problem
- Supervised training
Perceptron (McCulloch-Pitts)
Linear classification
- Points of different classes drawn on a graph can be demarcated by straight lines
Support vector machines (SVM): Can do linear classification
Q-Learning
Sketching
From here:
A sketch C(X) of some data set X with respect to some function f is a compression of X that allows us to compute, or approximately compute, f (X) given access only to C(X).
- 'compress data in a way that lets you answer queries' ยนยน
- Helps save bandwidth when streaming data over a network.