- Feedforward neural networks were the first type of artificial neural network invented and are simpler than their counterparts like recurrent neural networks and convolutional neural networks.
- They are among the simplest types of neural networks, yet they play a critical role in many applications, from image recognition to natural language processing.
Among the various types of neural networks, feedforward neural networks (FNNs) are among the most fundamental and widely used. They form the basis of many important neural networks being used in recent times, such as convolutional neural networks, recurrent neural networks and so on. Despite their simplicity, they form the backbone of many sophisticated AI systems. In this blog, we’ll explore what feedforward neural networks are and the core components of feedforward neural networks.
What is a feedforward neural network
A feedforward neural network is a type of artificial neural network where connections between nodes (neurons) do not form a cycle. This one-way flow of information—from the input layer, through hidden layers, to the output layer—is the defining feature of feedforward networks. Unlike recurrent neural networks (RNNs), which handle sequential data by looping connections, feedforward networks process data in a single pass, making them simpler and easier to understand.
Also read: 7 reasons why we use neural networks in machine learning
Also read: What are hidden layers in neural networks and what are their types?
Core components of feedforward neural networks
Input layer: The input layer is the first layer of the network, responsible for receiving and presenting the raw data or features from the dataset. Each node in this layer represents a feature or attribute of the data. For instance, in an image classification task, the input layer would receive pixel values from the image.
Hidden layers: Hidden layers are intermediate layers between the input and output layers. A feedforward neural network can have one or more hidden layers, each containing multiple neurons. Hidden layers perform complex computations and transformations on the input data. Each neuron in these layers calculates a weighted sum of the inputs, applies an activation function, and passes the result to the next layer. This process introduces non-linearity, allowing the network to learn and model intricate patterns in the data.
Output layer: The output layer produces the final result or prediction of the network. It transforms the data from the hidden layers into the desired output format. For classification tasks, the output layer might use a softmax activation function to provide probabilities for different classes. For regression tasks, it may use a linear activation function to predict continuous values.
Weights and biases: Weights and biases are parameters within the network that are adjusted during training. Weights determine the strength of connections between neurons, while biases allow the network to fit the data more flexibly. During training, the optimiser adjusts these parameters to minimise the loss function.
Activation functions: Activation functions introduce non-linearity into the network. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. These functions help the network learn from errors and capture complex relationships in the data.
