"UNDERSTANDING LINEAR NEURONS: FUNDAMENTALS AND LEARNING TECHNIQUES"

Abstract

Neural networks and deep learning have emerged as leading solutions for a myriad of supervised learning challenges. In 2006, Hinton, Osindero, and Teh introduced the concept of "deep" neural networks, a methodology that initiates with training a basic supervised model, subsequently augmenting it with additional layers and exclusively training the parameters of the new layer. This iterative process continues until a deep network is formed. Over time, the necessity for training one layer at a time has been transcended.

Contemporary Deep Neural Networks adopt a holistic approach, training all layers concurrently. Exemplary implementations include Tensor Flow, Torch, and Theano. Google's Tensor Flow, an open-source dataflow programming library, serves as a versatile tool for tasks spanning symbolic mathematics and machine learning applications, including neural networks. It is a staple for both research and production endeavors at Google. Torch, an open-source machine learning library and scientific computing framework, has established itself as an invaluable resource. Meanwhile, Theano, a numerical computation library for Python, contributes significantly to the numerical computations domain.

This unified training methodology across multiple layers imparts distinct advantages to neural networks, distinguishing them from other learning algorithms