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I am a third-year junior undergraduate in electrical engineering at the Indian Institute of Technology, Gandhinagar. Without any attempt to explore my major discipline, I started with coding, learning DSA and rushing behind code forces ratings. At the beginning of my second year, a core course in Signal Processing gave me a significantly different perspective, which I would say brought a meaning to my engineering journey. In an attempt to explore more, I started with a project in Digital signal processing in my early 2nd year without any knowledge of the direction ahead. Having a sense of the charm of Machine Learning, I decided to take up a course offered at our college in my third year. Simultaneously, I took up a Machine Learning project, which further polished my understanding.

In its most basic form, a neural network is a set of multilayered, interconnected nodes, often referred to as neurons. Each neuron receives input from neurons in the previous layer, performs a computation, and passes the result to the neurons in the next layer. The simplest neural network, a single-layer perceptron, consists of a set of input nodes, each connected to the output node. To address more complex tasks and learn non-linear relationships, multi-layer neural networks can be used. These networks consist of multiple layers of neurons, including one or more hidden layers between the input and output layers. These networks are often referred to as deep neural networks, given their multiple hidden layers.