11 Backpropagation Ipynb Colab

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. This assignment is due on Tuesday, Nov 05 2024 at 11:59pm PST. Starter code containing Colab notebooks can be downloaded here.

Note. Ensure you are periodically saving your notebook (File -> Save) so that you don’t lose your progress if you step away from the assignment and the Colab VM disconnects. Once you have completed all Colab notebooks except collect_submission.ipynb, proceed to the submission instructions. In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows: Now let us write (step by step) most general vectorized code using numpy (no loops will be used) to perform backward propagation on the convolution layer.

Note: The notations used can be found in the previous section (link to previous section). We are given the error \(dZ\) (partial derivative of the cost function \(J\) with respect to the output \(Z\)) and we need to find \(dX\), \(dK\) and \(db\) (Input and parameter gradients). I will not be going into the details of the derivation of Backpropagation (you can find it here in detail). In order to obtain \(dX\), it turns out that the backpropagation operation is identical to a stride = 1 convolution of a padded, dilated version of the output gradient \(dZ\) with a 180 degrees... This means, suppose following are the input matrix \(X\), kernel matrix \(K\) and output gradient of the layer \(dZ\): There was an error while loading.

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