The Fundamentals of Neural Networks
In the field of engineering, artificial or simulated neural networks are a subset of machine learning. They’re designed to mimic the neurons in a biological brain, which can send and receive signals to each other. Neural networks are generally composed of three layers: input, hidden, and output. These layers work together to receive and process the data given to them, with the goal of making accurate predictions and smarter decisions. Here’s a brief overview of this process:
- Data are provided to the input layer and then transferred to the hidden layer.
- The two layers communicate and randomly allocate weight, or importance, to each separate input.
- After the weights are multiplied, each input receives a bias and the weighted sum transfers to an activation function.
- This function decides which nodes should be triggered and “fired” to the next layer.
- The output layer receives an application function to distribute the output.
- The input weights are modified, and the output receives back-propagation to reduce the chance or errors.
Types of Neural Networks
Neural networks can be used in a variety of applications. Consider the types of smart technology that exist today, from Google Translate and face recognition to Netflix’s algorithm for recommending shows and movies. Here are just a few examples of neural networks:
Convolution – These are often used in image and video recognition applications because of their ability to identify patterns. They’re composed of convolutional, pooling, and fully connected layers.
Recurrent – Text-to-speech, translation, and grammar corrections in text all utilize recurrent neural networks. This method uses data from previous inputs to use in current inputs and outputs.
Multilayer perceptron – Speech recognition and machine translation are two common applications for multilayer perceptron neural networks. These are often used in deep learning because they contain dense and fully connected layers.