Archive | Deep Learning Performance

Line Plot Showing Single Model Accuracy (blue dots) vs Accuracy of Ensembles of Varying Size for Bagging

How to Create a Bagging Ensemble of Deep Learning Models in Keras

Ensemble learning are methods that combine the predictions from multiple models. It is important in ensemble learning that the models that comprise the ensemble are good, making different prediction errors. Predictions that are good in different ways can result in a prediction that is both more stable and often better than the predictions of any […]

Continue Reading 39
Line Plot Learning Curves of Model Accuracy on Train and Test Dataset Over Each Training Epoch

How to Develop an Ensemble of Deep Learning Models in Keras

Deep learning neural network models are highly flexible nonlinear algorithms capable of learning a near infinite number of mapping functions. A frustration with this flexibility is the high variance in a final model. The same neural network model trained on the same dataset may find one of many different possible “good enough” solutions each time […]

Continue Reading 37
Ensemble Methods to Reduce Variance and Improve Performance of Deep Learning Neural Networks

Ensemble Learning Methods for Deep Learning Neural Networks

How to Improve Performance By Combining Predictions From Multiple Models. Deep learning neural networks are nonlinear methods. They offer increased flexibility and can scale in proportion to the amount of training data available. A downside of this flexibility is that they learn via a stochastic training algorithm which means that they are sensitive to the […]

Continue Reading 39
Line Plots of Accuracy on Train and Test Datasets While Training With Dropout Regularization

How to Reduce Overfitting With Dropout Regularization in Keras

Dropout regularization is a computationally cheap way to regularize a deep neural network. Dropout works by probabilistically removing, or “dropping out,” inputs to a layer, which may be input variables in the data sample or activations from a previous layer. It has the effect of simulating a large number of networks with very different network […]

Continue Reading 19
A Gentle Introduction to Dropout for Regularizing Deep Neural Networks

A Gentle Introduction to Dropout for Regularizing Deep Neural Networks

Deep learning neural networks are likely to quickly overfit a training dataset with few examples. Ensembles of neural networks with different model configurations are known to reduce overfitting, but require the additional computational expense of training and maintaining multiple models. A single model can be used to simulate having a large number of different network […]

Continue Reading 28