Fully Connected vs Convolutional Neural Networks

Fully Connected vs Convolutional Neural Networks

In this post, we will cover the differences between a Fully connected neural network and a Convolutional neural network. We will focus on understanding the differences in terms of the model architecture and results obtained on the MNIST dataset.

In this post, we will cover the differences between a Fully connected neural network and a Convolutional neural network. We will focus on understanding the differences in terms of the model architecture and results obtained on the MNIST dataset.

Fully connected neural network

  • A fullyconnected neural network consists of a series of fullyconnected layers that connect every neuron in one layer to every neuron in the other layer.

  • The major advantage of fully connected networks is that they are “structure agnostic” i.e. there are no special assumptions needed to be made about the input.

  • While being structure agnostic makes fully connected networks very broadly applicable, such networks do tend to have weaker performance than special-purpose networks tuned to the structure of a problem space.

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Multilayer Deep Fully Connected Network, Image Source

Convolutional Neural Network

  • CNN architectures make the explicit assumption that the inputs are images, which allows encoding certain properties into the model architecture.
  • A simple CNN is a sequence of layers, and every layer of a CNN transforms one volume of activations to another through a differentiable function. Three main types of layers are used to build CNN architecture: Convolutional Layer, Pooling Layer, and Fully-Connected Layer.

To know more about the basic fundamentals related to CNN, check out my earlier blogs on Convolutions and Pooling.

neural-networks computer-vision keras deep-learning machine-learning

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