Activity regularization provides an approach to encourage a neural network to learn sparse features or internal representations of raw observations. […]
Archive | Deep Learning Performance
A Gentle Introduction to Activation Regularization in Deep Learning
Deep learning models are capable of automatically learning a rich internal representation from raw input data. This is called feature […]
How to Reduce Overfitting Using Weight Constraints in Keras
Weight constraints provide an approach to reduce the overfitting of a deep learning neural network model on the training data […]
A Gentle Introduction to Weight Constraints in Deep Learning
Weight regularization methods like weight decay introduce a penalty to the loss function when training a neural network to encourage […]
How to Use Weight Decay to Reduce Overfitting of Neural Network in Keras
Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data […]
Use Weight Regularization to Reduce Overfitting of Deep Learning Models
Neural networks learn a set of weights that best map inputs to outputs. A network with large network weights can […]
How to Configure the Number of Layers and Nodes in a Neural Network
Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers […]
Gentle Introduction to the Adam Optimization Algorithm for Deep Learning
The choice of optimization algorithm for your deep learning model can mean the difference between good results in minutes, hours, […]
How To Improve Deep Learning Performance
20 Tips, Tricks and Techniques That You Can Use To Fight Overfitting and Get Better Generalization How can you get […]