Use Jazzy to generate Swift documentation.

Format:

• /// description
• - parameter param: description
• - returns: xxxxxxx

run Jazzy

$ jazzy --min-acl internal --swift-version 2.1

Embed images to latex

\graphicspath{ {pic/} } % The folder containing images.
\includegraphics[scale=0.5]{imageName} % Embed a image.

CS229 Machine Learning - 3 (Linear regression, Probabilistic interpretation, Logistic regression, Newton's method)

Outline:
- Linear regression
- Probabilistic interpretation
- Logistic regression
- Newton's method

CS229 Machine Learning - 2 (Linear regression, Gradient descent, Normal equation)

Outline:
- Linear regression
- Gradient descent
- Normal equation

[Swift] Model View Controller

source: https://www.youtube.com/watch?v=QLJtT7eSykg&list=PLZYLTko_GQdGk39r8OTL-9lC92kd4AJy3&index=2

Elements of Reinforcement Learning

Elements of reinforcement learning:

• a policy: is a mapping from perceived states of the environment to actions to be taken when in those states.
• a reward function: defines the goal in a reinforcement learning problem. iIt maps each perceived state (or state-action pair) of the environment to a single number, a reward, indicating the intrinsic desirability of that state.
• a value function: specifies what is good in the long run. The value of a state is the total amount of reward an agent can expect to accumulate over the future, starting from that state.
• a model (optional)

reference: Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto