Deep Learning: Predicting Daily Bike Rental Riderships
License
By using this site, you agree to the Terms of Use that are defined in LICENSE.
About
The goal of this project is to build an Artificial Neural Network (ANN) from scratch to carry out a prediction problem on a real dataset. By building an ANN from the ground up, we'll have a much better understanding of gradient descent, back-propagation, and other concepts that are important to know before we move to higher-level tools such as PyTorch. We'll also get to see how to apply these networks to solve real prediction problems.
The data comes from the UCI Machine Learning database.
This project was developed in partial fulfillment of the requirements for Udacity's Deep Learning Nanodegree program.
Results
Prediction Losses
Predictions After Training
Installation
For best the experience with managing dependencies, we strongly advise you to install Anconda or Miniconda.
Create a virtual environment with conda:
conda create --name deep-learning python=3
Then, activate the environment:
conda activate deep-learning
Then, install the dependencies:
pip install -r requirements.txt
You can also download/clone this repository and launch the notebook using the following command:
jupyter-notebook Your_first_neural_network.ipynb
Usage
Run all code cells in the Your_first_neural_network.ipynb notebook file. Also have a look at the file my_answers.py for details on how to implement the ANN from scratch.