Examples of Initialization Techniques in Deep Learning

Initialization is a crucial step in deep learning that assigns initial values to a neural network's weights and biases before training. The choice of initialization technique can significantly impact a network's ability to learn and generalize. In this article, we'll explore the importance of initialization in deep learning and the common techniques used. To build a neural network using the three initialization methods described in the introduction, we will use the provided model() function. This function implements a three-layer neural network with a linear activation in the input layer, a rectified linear unit (ReLU) activation in the hidden layers, and a sigmoid activation in the output layer.