Let's dive into the AlexNet Architecture The AlexNet neural network architecture consists of 8 learned layers of which 5 are convolution layers, few are max-pooling layers, 3 are fully connected layers, and the output layer is a 1000 channel softmax layer. The pooling used here is Max pool. Why 1000 channels of softmax layer are taken?? This is because the Imagenet dataset contains 1000 different classes of images, so at the final output layer we have one node for each of these 1000 categories and the output layer is the softmax output layer. The input to the AlexNet network is a 227 x 227 size RGB image, so it's having 3 different channels- red, green, and blue.

Normalization has become an important of deep neural networks that compensates for the unbounded nature of certain activation functions such as ReLU, ELU etc. With these activation function, the output layers is not constrained within a bounded range (such as [-1,1] for tanh), rather they can grow as high as the training allows it. To limit the unbounded activation from increasing the output layer values, normalization is used just before the activation function. There are two common normalization techniques used in deep neural networks and are often misunderstood by the beginners. In this tutorial, a detailed explanation of both the normalization techniques will be discussed highlighting their key differences.

Convolutional Neural Networks (CNNs) have been doing wonders in the field of image recognition in recent times. CNN is a type of deep neural network in which the layers are connected using spatially organized patterns. This is in line with how the human visual cortex processes image data. Researchers have been working on coming up with better architectures over the last few years. In this blog post, we will discuss a particular type of layer that has been used consistently across many famous architectures.