Backpropagation applies the chain rule:
Chain rule:
[ \frac{dL}{dx} = \frac{dL}{dy} \cdot \frac{dy}{dx} ]
flowchart LR
x --> y
y --> L
Automatic differentiation computes exact derivatives efficiently using computational graphs.
Once we define an inner product, we can define the angle between two vectors.
Angles allow us to measure how aligned or different two vectors are in space.
Key Idea: Angle measures similarity between vectors. Orthogonality means complete independence (no similarity).
For vectors in n-dimensional space:
July 4, 2024Artificial Intelligence is often described in three stages, based on capability and scope:
examples
Artificial Intelligence (AI) is concerned with building systems that can perceive, reason, learn, decide and act in ways that achieve useful goals.
The foundations of AI, its major application areas, four classic ways of thinking about intelligence, important milestones in its development, and some of the risks that accompany increasingly capable AI systems.
flowchart TD
A[Artificial Intelligence] --> B[Human-like]
A --> C[Rational]
B --> D[Think like humans]
B --> E[Act like humans]
C --> F[Think rationally]
C --> G[Act rationally]
style A fill:#C8E6C9
style B fill:#E1F5FE
style C fill:#FFF9C4
style D fill:#EDE7F6
style E fill:#EDE7F6
style F fill:#C8E6C9
style G fill:#C8E6C9M2 → Search M4 → Game M5 → Knowledge → Fact → KB → Inference
Statistics: describes data (what you see).
Probability: models uncertainty (what you don’t know yet).
flowchart TD
A[Dataset] --> B[Central Tendency]
A --> C[Variability]
B --> B1[Mean]
B --> B2[Median]
B --> B3[Mode]
C --> C1[Range]
C --> C2[Variance]
C --> C3[Standard Deviation]
C --> C4[IQR]
Central tendency tells you where the “middle” of the data is. Describes a set of scores with a single number that describes the PERFORMANCE of the group.
Machine learning system optimisation begins with measurement. Before changing an algorithm, adding processors, or moving work to a GPU, we need to understand what is slow, which resource is limiting performance, and how performance changes as the workload grows.
This page covers:
By the end of this page, you should be able to:
Reinforcement Learning (RL) is a learning approach in which an agent improves its behaviour by interacting with an environment and observing the rewards produced by its actions.
Unlike supervised learning, the agent is not given a correct action label for every situation. It must discover useful behaviour through trial, feedback, and repeated interaction.
Reinforcement learning is goal-oriented learning through interaction.RL is useful when decisions affect what happens next and the quality of an action may only become clear later.