Types of Learning
Last updated
Last updated
There are 3 broad types of learning.
This is the case where we have an output variable (y) acting as a supervisor. For instance, in the handwriting recognition scenario
Where we have a large data set of alphabets with labels ranging from 'a' to 'z',
The labels acting as the 'supervisor' for correct or incorrect labeling of characters, which
Ultimately aids in designing the best hypothesis (g) to predict the output variable (label) of the next instance of data
This is the where we do not have the luxury of an output variable (y) acting as a supervisor. Hence, in the same handwriting recognition scenario,
Given a large data set of characters, the machine is likely to derive unknown clusters of alphabets looking alike,
For example, "1", "i", "I", and "l" being in the same cluster for i.e. their symmetry / intensity (will be discussed subsequently)
Similar to supervised learning, a feedback is given here. However, there is no data acting as explicitly as a supervisor. Reinforcement learning merely rewards and penalizes a model for making/deviating from its progress towards the desired output.
For example:
We can teach self-driving cars to drive by giving them rewards
For instance where a positive result is given, we can give them a positive reward (score), and
where an undesirable result is obtained, we can give a negative reward (score) to that
