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+# Summary of Gradient Descent
+
+For a two layer network. The `[]`s denote the layer number.
+`'` denotes prime. `T` denotes transpose.
+
+## Scalar implementation
+
+```
+dz[2] = a[2] - y
+dW[2] = dz[2]a[1]T
+db[2] = dz[2]
+dz[1] = W[2]Tdz[2] * g[1]'(z[1])
+dW[1] = dz[1]xT
+db[1] = dz[1]
+```
+
+
+## Vectorized Implementation
+
+```
+dZ[2] = A[2] - Y
+dW[2] = (1/m)dZ[2]A[1]T
+db[2] = (1/m)*np.sum(dZ[2], axis=1, keepdims=True)
+dZ[1] = W[2]TdZ[2] * g[1]'(z[1])
+db[1] = (1/m)*np.sum(dZ[1], axis=1, keepdims=True)
+```