Standard feedforward neural network with fully-connected layers to solve classification and regression problems.
- Single/Multioutput Regression
- Binary/Multilabel/Multiclass Classification
- L1/L2 Regularisation
- Dropout Regularisation
- Mini-Batch Gradient Descent (GD)
- Adam Optimisation (Momentum GD + RMS Propagation)
- Learning Rate Decay
The baseline verification was performed with Scikit-Learn's MLPClassifier on a generated dataset (3 features, 4 classes). Test accuracy scores for both models were about 0.9, with comparable runtimes.