In this project, you'll get 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, 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 Tensorflow. 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.
Download the project materials from this link, or from the link at the bottom of the page, and unzip it. cd into the dlnd-your-first-network folder that you just unzipped. 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:
jupyter notebook dlnd-your-first-neural-network.ipynb
Follow the instructions in the notebook will lead you through the project. Ensure you've passed the unit tests in the notebook before you submit the project! If you need help running the notebook file, check out the Jupyter notebook lesson.