Predicting Bike Sharing Data

First Neural Network

A neural network built from scratch to carry out a prediction problem on a real dataset to predict how many bikes a business would need according to the historical data so that it doesn't lose out money from potential riders by having too few bikes and also doesn't waste money on bikes that are just sitting around by having too many.

Open and view the Project using the .zip file provided or at my Github Repository.

The project is also hosted on Github.

Table of Contents

• Getting Started
  • Tools Required
  • Installation
• Running the App
• Development
• Stopping the App
• Extras
• References

Getting Started

The starter project can be downloaded from here

The project will be evaluated by a Udacity code reviewer according to the project rubric

The neural network is trained on the UCI Machine Learning Dataset

This is how the neural network will look:

The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node.

Tools Required

You would require the following tools to develop and run the project:

• Pip
• Python
• Anaconda or Miniconda

Installation

With Conda

• Start by installing python and anaconda

• Create a new conda environment

   conda create --name dlnd python=3
  

• Enter your new environment:

  • Mac/Linux: >> source activate dlnd
  • Windows: >> conda activate dlnd

• Install numpy, matplotlib, pandas, and jupyter notebook using the following command:

   conda install numpy matplotlib pandas jupyter notebook
  

Without Conda

If you don't have Conda, a requirements.txt file is provided to install all of the necessary packages using pip.

Open CLI in the root directory of the project. Run the following command:

python -m venv --system-site-packages .\venv # Creates a virtual environment
.\venv\Scripts\activate                      # Activates the environment
pip install -r requirements.txt              # Installs the required packages
pip list                                     # Show packages installed within the virtual environment

Running the App

To run the project:

1. Activate the Conda or Python virtual environment as mentioned above

2. Start the Jupyter Notebook by running the following command:

   jupyter notebook
   

   Open your browser and visit localhost:8888 (or the port indicated in the terminal), and you should see all of the contents of the project in Your_first_neural_network.ipynb notebook

3. After completing the development, press the play ▶️ icon to start the execution of cells. The output will be visible right below each respective cells.

Development

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

Stopping the App

Once you're done with the app, stop it gracefully using the following command:

1. Select File -> Close and Halt inside jupyter notebook
2. Press Ctrl+c in the cli

With Conda

>> conda deactivate dlnd           # Deactivate the environment
>> conda remove --name dlnd --all  # Delete the environment

Without Conda

>> deactivate                      # Deactivate the environment

Extras

• Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback.
• The setup for the project assistant is simple. If you have not installed the client tool from a different Nanodegree program already, then you may do so with the command: pip install udacity-pa
• To submit your code to the project assistant, run udacity submit from within the top-level directory of the project. You will be prompted for a username and password.
• This process will create a zipfile in your top-level directory named first_neural_network-result-.zip, where there will be a number between result- and .zip. This is the file that you should submit to the Udacity reviews system.

References

• Gradient by Khan Academy
• Multivariable calculus lessons by Khan Academy
• Backpropogation Lecture from Andrej Karpathy