Teaching machine learning!
Getting machine learning up and running is similar to picking up other external libraries. Take on these projects and you'll have solved problems using neural networks, random forests, and Support Vector Machines!
learningMachines/neuralNet/server/neuralNet/train.csv
, and make sure it is named train.csv
nodemon server.js
You can now make api calls to this server, either through your browser (http://localhost:5000/neuralNet/startNet
), or through curl on your command line (curl localhost:5000/neuralNet/startNet
)
neuralNetLogic.js
is where we have all the actual JS logic built out.
Here are the things I expect you to do
Parallelize the training of multiple nets at the same time. Training each net is synchronous, so parallelizing won't help you train a single net any faster. But you could try creating multiple versions that have different parameters (number of nodes, hidden layers, learning rate, etc.) and train those in parallel with each other.
Build out grid search to try different combinations of number of hidden layers and number of nodes. Trying different combinations of hyperparameters (the parameters that determine the shape or conditions of the algorithm, such as number of nodes, or number of hidden layers) to find the optimal set is called grid search. scikit-learn has a good module explaining and implementing grid search. We can't use their implementation direction, but it's a good explanation of the high-level concept.