Once in the Apporto environment, create a new Jupyter Notebook and configure it using the following naming convention:

$\_\_$Assignment$3.$ipynb

Thus, if your name is Jane Doe, please name the submission file $$Doe$\_$Jane$\_$Assignment$3.$ipynb$.$

For information on how to navigate the Jupyter environment, review the Jupyter Notebook in Apporto $($Virtual Lab$)$ Tutorial.

Read through the $$Recognizing CIFAR$-10$ images with deep learning$$ example on pages $8494$ of Deep Learning with Keras. Copy the code into your Jupyter Notebook, including the code for a deeper network and data augmentation in your model. Run the code to build a convolutional neural network model that includes a deeper network and data augmentation.

Note: More information about the training and test data sets can be found in the CIFAR$-10$ and CIFAR$-100$ Datasets repository.

The algorithm you worked to train can be used to distinguish more realistic images than the hand$-$written digits example from the previous module. While distinguishing between animals or vehicles may not pose a serious ethical dilemma, it$$s important to consider what other types of images an algorithm such as this could be trained on$.$ For example, could such an algorithm eventually be used to distinguish people$$s faces? If so$,$ what are the ethical and privacy implications? Create a Markdown cell in your Jupyter Notebook after your code and its outputs. In this cell, analyze the ethical and privacy implications of the algorithm you just created. You are expected to include resources to support your answers, and must include citations for those resources.