Solving the Vanishing Gradient Problem with Self-Normalizing Neural Networks using Keras

Training deep neural networks can be a challenging task, especially for very deep models. A major part of this difficulty is due to the instability of the gradients computed via backpropagation. In this post, we will learn how to create a self-normalizing deep feed-forward neural network using Keras. This will solve the gradient instability issue, speeding up training convergence, and improving model performance. Disclaimer: This article is a brief summary with focus on implementation.