Coursera Machine Learning : W4 Neural Network

W4 Neural Network

Notes

Neural Network

• Non-linear classification: Logistic regression may take in too many polynomial features.
• Neuron: Dendrite (input wires), Axon (output wires)
• Logistic unit: x0 –> bias unit
• An artificial neuron (single) with a sigmoid activation function
• Neural Network: Input layer, Hidden layer, and Output layer.
• a^(j)_i “activation” of unit i in layer j.
• theta^(j) = matrix of weights controlling function mapping from layer j to j+1.
• If network has s_j units in layer j and s_j+1 units in layer j+1, the theta j would be of dimension s<sub>j+1</sub>*s<sub>j</sub>+1
• Important Forward propagation representation

• Be careful with the MATLAB syntax: for i = 1:10 COLON not COMMA!

• negation 否定
• parenthese 括号
• auditory cortex 听觉皮层