In this project, I got to 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, I have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before mastering higher level toolssuch as Tensorflow. I also got to see how to apply these networks to solve real prediction problems!
The data comes from the UCI Machine Learning Database.
- Download anaconda or miniconda based on the instructions in the Anaconda lesson.
- Create a new conda environment:
conda create --name dlnd python=3- Enter your new environment:
- Mac/Linux: >> source activate dlnd
- Windows: >> activate dlnd
- Ensure you have numpy, matplotlib, pandas, and jupyter notebook installed by doing the following:
conda install numpy matplotlib pandas jupyter notebook- Run the following to open up the notebook server:
jupyter notebook- In your browser, open Your_first_neural_network.ipynb
- Udacity - Initial work - Deep Learning Repo
- Jonathan Sulivan
- Hackbright Academy
- Udacity
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