Lets try to generalize a sine wave using neural network. The NN will learn the data and will approximate. We will need atleast 20 hidden neurons for the NN to model properly. For a NN to generalize to a non-linear output, it needs atleast 20 neurons in the hidden layer.
I used about 20 neurons in the hidden layer and trained for about 800 epocs below you see the result of the learning capability of the neural network.
Red – Original Training data
Blue – NN output after learning
- Tutorial: Pybrain Neural network for classifying Olivetti faces
- Facial keypoints extraction using Caffe
- Running a neural network in GPU
- Machine learning using Restricted Boltzmann machines
- Tutorial: Titanic dataset machine learning for Kaggle
- Install FANN neural network in Mac
- Scikit Machine learning of Car evaluation dataset
- How to setup Caffe to run Deep Neural Network
- How Harassment Training Helps Reduce Workplace Issues