Key Role Players in Artificial Neural Networks: An Overview

The computational world of machine learning (ML) has been transformed by two simple key role players in the signal processing domain: sampling and convolution. Sampling and convolution are highly mathematical yet are the most significant signal-processing techniques. Digital signal processing has reduced the complexity of the analog processing with approximations and encouraged intelligent humans to consider these powerful role players, sampling, and convolution. Their applications are diverse and affect the world we see today. Sampling has changed the way the data are processed, stored, and transmitted. The sub-processes of sampling, such as interpolation and decimation, have the advantage of changing the sampling rate within a system and work effectively in the wavelet transform. It helps to store the two versions of the digital image with approximate and detailed coefficients and achieves a remarkable compression of data in this high-resolution world with the help of multi rate sampling. On the extended line, convolution has played a large role in identifying features and has led to a deep understanding of human intelligence and cognitive science. The understanding of the features of deep learning has been strongly affected by convolution. This article focuses on these two signal-processing techniques and their role in the transformation of machine learning algorithms into deep learning techniques.