In this project, you'll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, you'll have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before we move to higher-level tools such as PyTorch. You'll also get to see how to apply these networks to solve real prediction problems!
The data comes from the UCI Machine Learning Database.
Final Project Notebook
Download the project materials from our GitHub repository. You can get download the repository with git clone https://github.com/udacity/deep-learning-v2-pytorch.git. Our files in the GitHub repo are the most up to date, so it's the best place to get the project files.
cd into the project-bikesharing directory.
Download anaconda or miniconda based on the instructions in the Anaconda lesson. These are also outlined in the repository README.
Create a new conda environment: conda create --name deep-learning python=3
Enter your new environment: Mac/Linux: >> source activate deep-learning Windows: >> activate deep-learning
Ensure you have numpy, matplotlib, pandas, and jupyter notebook installed by doing the following: conda install numpy matplotlib pandas jupyter notebook
Run the following to open up the notebook server: jupyter notebook
In your browser, open Predicting_bike_sharing_data.ipynb. Note that in the previous workspace this was called Your_first_neural_network.ipynb but the contents are the same, this is just a descriptive difference.
Follow the instructions in the notebook; they will lead you through the project. You'll ultimately be editing the my_answers.py python file, whose components are imported into the notebook at various places.