This assignment is due on May 11 at 11:59pm PST.

Starter code containing Colab notebooks can be downloaded here.

• Setup
• Goals
• Q0: Fully Connected Nets (Continued)
• Q1: Batch Normalization
• Q2: Dropout
• Q3: Convolutional Networks
• Submitting your work

Setup

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.

Goals

In this assignment you will practice writing backpropagation code and training CNNs. The goals of this assignment are as follows:

• Practice hyperparameter tuning while training a well-performing neural net.
• Implement Batch Normalization and Layer Normalization for training deep networks.
• Implement Dropout to regularize networks.
• Understand the architecture of Convolutional Neural Networks and get practice with training them.

Q0: Fully Connected Nets (Continued)

In the notebook FullyConnectedNetsContinued.ipynb you will use tools from A2 to train a neural network.

Q1: Batch Normalization

In the notebook BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully-connected networks.

Q2: Dropout

The notebook Dropout.ipynb will help you implement Dropout and explore its effects on model generalization.

Q3: Convolutional Networks

In the notebook ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks.

Submitting your work

Important. Please make sure that the submitted notebooks have been run and the cell outputs are visible.

1. Open collect_submission.ipynb in Colab and execute the notebook cells.

This notebook/script will generate a zip file of your code (.py and .ipynb) called a3_code_submission.zip..

If your submission for this step was successful, you should see the following display message:

### Done! Please submit a3_code_submission.zip to Gradescope. ###

2. Submit the zip file to Gradescope.

Remember to download a3_code_submission.zip locally before submitting to Gradescope.

3. Ensure that you have answered, on Gradescope, the inline questions scattered throughout the notebooks.