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= machine learning = | = machine learning = | ||
* | == supervised learning == | ||
* unsupervised learning | * 학습을 시킬 때 label에 정답이 있는 것 | ||
* reinforcement learning | * Need input, target | ||
* Learning from difference between prediction and target | |||
* e.g. mnist, classification | |||
== unsupervised learning == | |||
* label 이 미리 정해져 있지 않은 것 | |||
* Need input | |||
* Cluster by distance between inputs | |||
* Can't predict outcome | |||
* e.g. clustering | |||
== reinforcement learning == | |||
* 일종의 unsupervised learning | |||
* input : environment, reward, output : action | |||
* Learn from try | |||
* Model free | |||
* e.g. game play, stock trading | |||
== reinforcement learning == | |||
* Q learning | |||
* Neural Network | |||
* DQN : Deep Q Learning | |||
== Basic knowledge | |||
* MDP : Markov Decision Process | |||
* Bellman equation | |||
* Dynamic programming | |||
* Value, Polish | |||
* Value function, Polish function | |||
* Value iteration, Polish iteration | |||
== | |||
Revision as of 03:28, 1 July 2017
machine learning
supervised learning
- 학습을 시킬 때 label에 정답이 있는 것
- Need input, target
- Learning from difference between prediction and target
- e.g. mnist, classification
unsupervised learning
- label 이 미리 정해져 있지 않은 것
- Need input
- Cluster by distance between inputs
- Can't predict outcome
- e.g. clustering
reinforcement learning
- 일종의 unsupervised learning
- input : environment, reward, output : action
- Learn from try
- Model free
- e.g. game play, stock trading
reinforcement learning
- Q learning
- Neural Network
- DQN : Deep Q Learning
== Basic knowledge
- MDP : Markov Decision Process
- Bellman equation
- Dynamic programming
- Value, Polish
- Value function, Polish function
- Value iteration, Polish iteration
==