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.
[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
[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.