Tag: Neural Network Regularization

In this tutorial, we will be implementing the Deep Convolutional Generative Adversarial Network architecture (DCGAN). We will go through the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks first. This paper by Alec Radford, Luke Metz, and Soumith Chintala was released in 2016 and has become the baseline for many Convolutional GAN architectures […] ...

In this article, we will discuss how to get per-class accuracy in a highly imbalanced image/vision dataset. Deep learning algorithms suffer when the dataset is highly imbalanced. In image recognition, a deep neural network may predict 90% of one class correctly and only 20% of another class correctly. This is the result of the number […] ...

Deep Learning neural networks can work well for several tasks today. We can use deep neural networks for image recognition, object detection, segmentation, speech, NLP and much more. But there is one problem with deep neural networks. They are bad at handling noise during real-world testing. The generalization power of deep neural networks reduces drastically […] ...