Helps you find a suitable neural network configuration for deep learning on time series.
* Provides starting point for researchers to use deep learning * Creates deep learning models for classification and regression on time series data * Derives features automatically from raw data * Helps with finding a suitable model architecture and hyperparameters * Has a tutorial in Python to get you started! Deep learning is a powerful tool to help with automated classification or regression tasks. However, designing a deep learning network that works well for your data is not trivial: it requires the user to choose the number of layers in the network, the number of nodes in each layer, the type of each layer, and so forth. With so many degrees of freedom, finding the network that is right for your data is an arduous task. Moreover, each network still needs to be calibrated or _trained_ before it can be usefully applied to automated classification or regression tasks. mcfly simplifies this process by making explicit the required steps while offering useful default values at each step. mcfly then proceeds by trying out many different network configurations, training each one to the data provided by the user. It subsequently lists the performance of each network, along with a visualization that helps the user judge each network's tendency to overfit or underfit the data.
Mcfly: Automated deep learning on time series