# The one page method map

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/read/19/tee-c19-s0

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c19-s0/

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