The video discusses:
- handwritten digit recognition example
- kernel size 28x28 pixel = 784 array
- activation layer 784 (1-dimensional) array
- 2 hidden layers, each with 16 neurons (arbitrary choice)
- hidden layers correspond to macro and micro features within the digit
- output layer 0..9, the largest value is the most likely selection of the NN
- He notes that the same principle can be applied to speech recognition.
- neuron parameters: knobs to adjust
- weights: connections between neurons
- activation function: normalizes the values between 0..1
https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=3
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