Deep Learning · TEE · section

The one page method map

Formula reference and exam revision

Question clue	First action	Core formula or check
Neuron output	Multiply each input by its matching weight	LaTeX: z=\sum_iw_ix_i+b, then activate
Network prediction	Finish one whole layer before the next	LaTeX: h=f(Wx+b)
Backpropagation	State loss then prediction-minus-target	delta times sending activation
Optimizer update	Identify current gradient and stored state	old parameter minus update
CNN dimensions	Write N K S P and channels	LaTeX: \left\lfloor\frac{N+2P-K}{S}\right\rfloor+1
CNN parameters	Count one filter then all filters	LaTeX: (K^2C_{\mathrm{in}}+1)C_{\mathrm{out}}
Pooling	Draw every valid window	Maximum, or mean of the window
Dense parameters	Count connections and receiving biases	LaTeX: (N_{\mathrm{in}}+1)N_{\mathrm{out}}
RNN hidden state	Add new-input and old-state transforms	LaTeX: \tanh(W_{xh}x+W_{hh}h_{t-1}+b)
LSTM	Retain old add new then expose	Retain old cell, add admitted candidate, then apply output gate
GRU	Check the stated z convention	LaTeX: h=(1-z)h_{t-1}+z\widetilde h