Examples / Demos
Here are some demos running directly in the browser:
- Redo gradient descent video about
- Delta weight formulas, connect to "mathematics of gradient" video
- Implement gradient descent in library / with code
- XOR coding challenge live example
- MNIST coding challenge live example
- redo this challenge
- cover softmax activation, cross-entropy
- graph cost function?
- only use testing data
- Support for saving / restoring network (see #50)
- Support for different activation functions (see #45, #62)
- Support for multiple hidden layers (see #61)
- Support for neuro-evolution
- play flappy bird (many players at once).
- play pong (many game simulations at once)
- steering sensors (a la Jabril's forrest project!)
- Combine with ml5 / deeplearnjs
If you're looking for the original source code to match the videos visit this repo
You need to have the following installed:
- Install the NodeJS dependencies via the following command:
This Project doesn't require any additional Installing steps
NeuralNetwork- The neural network class
predict(input_array)- Returns the output of a neural network
train(input_array, target_array)- Trains a neural network
Running the tests
The Tests can either be checked via the automaticly running CircleCI Tests or you can also run
npm test on your PC after you have done the Step "Prerequisites"
Please send PullRequests. These need to pass a automated Test first and after it will get reviewed and on that review either denied or accepted.
- shiffman - Initial work - shiffman
See also the list of contributors who participated in this project.
This project is licensed under the terms of the MIT license, see LICENSE.