Help a bike-sharing company make business decisions
Returns the expected volume of business based on historical bike-rental data, so the company can predict the number of bikes they will need. First project for the Udacity Deep Learning Nanodegree.
my_answers.py and see my results in bike_sharing.ipynb.Step 1: Review my code in
Step 2: Do this project yourself
To make your own Bike-sharing Predictor, go here and clone the repository.
git clone https://github.com/udacity/deep-learning-v2-pytorch.git cd deep-learning-v2-pytorch/project-tv-script-generation
Then open Predicting_bike_sharing_data.ipynb.
- Jupyter Notebooks
- The bike-sharing dataset from the UCI Machine Learning Database.
- PyTorch and Torchvision. For installation instructions see Udacity's README in the Deep Learning repository.
Watch out for size mismatch.
Almost all the challenges I ran into with this project happened as a result of size mismatch. Read the comments in my code to see where I had these problems and how I solved them.
To pass all unit tests, I needed to add this print statement to the test_run function under unittests in bike_sharing.ipynb.
The print statment should be added here:
def test_run(self): # Test correctness of run method network = NeuralNetwork(3, 2, 1, 0.5) network.weights_input_to_hidden = test_w_i_h.copy() network.weights_hidden_to_output = test_w_h_o.copy() print(network.run(inputs)) #Add the print statement here self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
- @technoempathy – Layla Messner
- @udacity for the project
- @facebook for the scholarship to the Udacity Deep Learning Nanodegree