rifkimubarok/predicting-bike-sharing-patterns

In this project, I 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.

Predicting-Bike-Sharing-Patterns

In this project, I 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.

Instructions

[1]. Download the project materials from our GitHub repository. You can get download the repository with git clone https://github.com/ravijyoti3/Predicting-Bike-Sharing-Patterns. Our files in the GitHub repo are the most up to date, so it's the best place to get the project files.

[2]. cd into the project-bikesharing directory.

[3]. Download anaconda or miniconda based on the instructions in the Anaconda lesson. These are also outlined in the repository README.

[4]. Create a new conda environment:

[5]. conda create --name deep-learning python=3

[6]. 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:

[7]. conda install numpy matplotlib pandas jupyter notebook

[8]. Run the following to open up the notebook server: jupyter notebook

[9]. 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.

[10]. 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.