A notebook that is meant to teach people how to understand, design, and create neural networks. Goes over history, forward pass, backpropagation, gradient descent, optimizers, regularization techniques, and hyperparameters, as well as theory.
Ensure that you have some sort of python and jupyter notebook environment installed. Also make sure you have git installed. You can go here to install git.
- Step 1: Create a folder you want the file to be.
- Step 2: Open your command line of choice (powershell, cmd, mac alternatives, linux, etc.)
- Step 3: Navigate to project folder with
cd yourFolderName - Step 4: Clone the repository with
git clone https://github.com/PatrickErickson4/NeuralNetLesson.git - Step 5: Install the dependencies with
pip install -r requirements.txt
- Step 1: Open NeuralNetGuide.ipynb. Make sure you have jupyter notebooks on your environment.
- Step 2: Select your interpreter option, and run the code blocks as you go through the article. Play with the simulations, and have fun!
The following was produced under the MIT license.
- Python scripts were generated with the assistance of AI.