Neural Network Week 1 & Week 2

1. Different types of neurons

  • Linear neurons
  • Binary threshold neurons
  • Recitified linear neurons
  • sigmoid neurons
  • Stochastic binary neurons

 

2. Reinforcement learning

Learn to select an action to maximize payoff.

– The goal in selecting each action is to maximize the expected sum
of the future rewards.
– We usually use a discount factor for delayed rewards so that we
don’t have to look too far into the future.

Reinforcement learning is difficult:

– The rewards are typically delayed so its hard to know where we
went wrong (or right).
– A scalar reward does not supply much information.

 

3. Main types of neural networks architecture

  • Feed-forward  
    • The first layer is the input and the last layer is the output
    • They compute a series of transformations that change the similarities between cases
    • The activities of the neurons in each layer are a non-linear function of the activities in the layer below

　　

  • Recurrent
    • They have directed cycles in their connection graph
    • They have complicated dynamics
    • It is a very natural way to model sequential data
      • They are equivalent to very deep nets with one hidden layer per time slice
      • They use the same weights at every time slice and they get input at every time slice.
    • They have the ability to remember information in their hidden state

      

     

  • Symmetrically connected networks
    • They are like recurrent networks but the connections between units are symmetrical(same weights in both directions)

 

4. Perceptrons

  • Add an extra component with value 1 to each input vector. The “bias” weight on this component is minus the threshold. Now we can forget the threshold.
  • Pick training cases using any policy that ensures that every training case willkeep getting picked.
    • If the output unit is correct, leave its weights alone
    • If the output unit incorrectly outputs a 1, subtract the input vector from the weight vector
    • If the output unit incorrectly outputs a zero, add the input vector to the weight vector.
  •  This is guaranteed to find a set of weights that gets the right answer for all the
    training cases if any such set exists.

 

5. The limitations of Perceptrons

  • once the hand-coded features have been determined, there are very strong limitations on what a perceptron can learn

  • the part of a Perceptron that learns cannot learn to do this if the transformations form a group

 

posted @ 2017-07-01 09:52  ClimberClimb  阅读(118)  评论(0)  编辑  收藏  举报