Notebook: Analyzing Student Data

Now, we're ready to put neural networks in practice. We'll analyze a dataset of student admissions at UCLA.

To open this notebook, you have two options:

• Go to the next page in the classroom (recommended).
• Clone the repo from Github and open the notebook StudentAdmissions.ipynb in the intro-neural-networks > student_admissions folder. You can either download the repository with git clone https://github.com/udacity/deep-learning-v2-pytorch.git , or download it as an archive file from this link .

Instructions

In this notebook, you'll be implementing some of the steps in the training of the neural network, namely:

• One-hot encoding the data
• Scaling the data
• Writing the backpropagation step

This is a self-assessed lab. If you need any help or want to check your answers, feel free to check out the solutions notebook in the same folder, or by clicking here .