An interesting benefit of deep learning neural networks is that they can be reused on related problems. Transfer learning refers […]
Archive | Deep Learning Performance
How to Avoid Exploding Gradients With Gradient Clipping
Training a neural network can become unstable given the choice of error function, learning rate, or even the scale of […]
How to use Data Scaling Improve Deep Learning Model Stability and Performance
Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. The weights of […]
How to Use Greedy Layer-Wise Pretraining in Deep Learning Neural Networks
Training deep neural networks was traditionally challenging as the vanishing gradient meant that weights in layers close to the input […]
How to Choose Loss Functions When Training Deep Learning Neural Networks
Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. As part of the optimization algorithm, the […]
Loss and Loss Functions for Training Deep Learning Neural Networks
Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring […]
Understand the Impact of Learning Rate on Neural Network Performance
Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that […]
How to Configure the Learning Rate When Training Deep Learning Neural Networks
The weights of a neural network cannot be calculated using an analytical method. Instead, the weights must be discovered via […]
How to Control the Stability of Training Neural Networks With the Batch Size
Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated […]
How to Accelerate Learning of Deep Neural Networks With Batch Normalization
Batch normalization is a technique designed to automatically standardize the inputs to a layer in a deep learning neural network. […]