What are L1, L2 and Elastic Net Regularization in neural networks?
When you’re training a neural network, you’re learning a mapping from some input value to a corresponding expected output value....
How to use Batch Normalization with Keras?
The inputs to individual layers in a neural network can be normalized to speed up training. This process, called Batch...
What is Batch Normalization for training neural networks?
Training neural networks is an art rather than a process with a fixed outcome. You don’t know whether you’ll end...
Making more datasets available for Keras
Thanks to modern deep learning frameworks like Keras, it’s very easy to use particular datasets – which are included in...
How does the Softmax activation function work?
When you’re creating a neural network for classification, you’re likely trying to solve either a binary or a multiclass classification...
Visualizing Keras neural networks with Net2Vis and Docker
Visualizing the structure of your neural network is quite useful for publications, such as papers and blogs. Today, various tools...
How to use FTSwish with Keras?
Flatten-T Swish is a new (2018) activation function that attempts to find the best of both worlds between traditional ReLU...
What is the FTSwish activation function?
Over the last few years, we’ve seen the rise of a wide range of activation functions – such as FTSwish....
Exploring the Keras Datasets
Datasets are crucial to functional machine learning models. Having a good dataset available can be the difference between success or...
How to create a variational autoencoder with Keras?
In a different blog post, we studied the concept of a Variational Autoencoder (or VAE) in detail. The models, which...
