2. Forward and Backward Max Pooling

3. Upsampling in Pooling

4. Convolution Operation (Forward Pass)

5. Input Size : 3x3, Filter Size : 2x2, Output Size : 2x2
Output Equations

6. Derivative Computation (Backward Pass)
Final derivatives after performing back propagation
Note: no deconvolution, we are trying to compute an update of weight

7. For a full derivation, go through this site
Only Numpy: Understanding Back Propagation for Transpose Convolution in Multi Layer CNN with Example and Interactive Code.
back-propagation-for-transpose-convolution-in-multi-layer- cnn-with-c0a07d191981