Neural Networks

A calculator multiplies two thousand-digit numbers and takes the remainder without thinking about it. Ask a neural network to do the same thing and it becomes an open research question, one that Terence Tao helped set as a competition. The answer has to be exact, because a remainder that is off by one is simply wrong. This is what I submitted to the SAIR Foundation’s Modular Arithmetic Challenge, why the obvious approach cannot work, and the one idea that made the difference.

The mathematics of the Transformer, worked through from the 2017 paper by Vaswani and colleagues. Scaled dot-product attention, why the scaling factor is there, multi-head attention, positional encoding, and the encoder and decoder stacks, each derived rather than asserted, for a reader who wants to understand the architecture rather than only use it.

Special thanks to Karan Dhiman for his meaningful contributions, support, and wisdom that helped shape this work. Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. Deep learning’s breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.

Special thanks to Archit Konde for his meaningful contributions, support, and wisdom that helped shape this work. The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs).