This paper proposes a novel end-to-end deep neural network architecture and adopts Gumbel distribution as an activation function in neural networks for class imbalance problem in the application of ...

Wrapping Up Binary classification is arguably the most fundamental problem in machine learning. There are several alternatives to using a neural network. Logistic regression is perhaps the most common ...

In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the success of neural ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of ...

Deep learning is an important part of machine learning, and the deep learning algorithms are based on neural networks. There are several neural network architectures with different features, suited ...

Deep learning's availability of large data and compute power makes it far better than any of the classical machine learning algorithms.

The differences between neural network binary classification and multinomial classification are surprisingly tricky. In this article I explain two different approaches to implement neural network ...