The Essential Guide to Neural Network Architectures

Ready? Let's start with the basics. Neural Networks are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. The input data is processed through different layers of artificial neurons stacked together to produce the desired output. From speech recognition and person recognition to healthcare and marketing, Neural Networks have been used in a varied set of domains. The Neural Network architecture is made of individual units called neurons that mimic the biological behavior of the brain. Here are the various components of a neuron. Input - It is the set of features that are fed into the model for the learning process. For example, the input in object detection can be an array of pixel values pertaining to an image. Weight - Its main function is to give importance to those features that contribute more towards the learning. It does so by introducing scalar multiplication between the input value and the weight matrix. For example, a negative word would impact the decision of the sentiment analysis model more than a pair of neutral words. Transfer function - The job of the transfer function is to combine multiple inputs into one output value so that the activation function can be applied.