Mathematical-Functions-in-Neural-Networks

The primary objective of this paper is to explore and analyze the essential mathematical functions and equations that underpin neural networks

Mathematical Functions and Equations in Neural Networks: A Comprehensive Overview

Table of Contents:

1. Introduction
2. Importance of Mathematical Functions in Neural Networks
3. Key Mathematical Functions in Neural Networks A. Activation Functions B. Loss Functions C. Optimization Algorithms
4. Mathematical Equations in Neural Networks
5. Gradient Descent and Its Variants
6. Conclusion

Introduction

Neural networks are computational systems inspired by the structure and functioning of the human brain. These networks consist of processing units called neurons, which are connected in layers. Each neuron receives inputs, applies an activation function, and produces an output that is transmitted to other neurons. Neural networks use learning algorithms such as backpropagation to adjust the weights between neurons and improve their performance based on data. These networks are capable of simulating complex patterns and are used in various tasks such as natural language processing, computer vision, pattern recognition, and analyzing complex datasets. Since neural networks can respond to data and improve themselves, they can make intelligent predictions and decisions without requiring precise programming. For this reason, neural networks have become one of the main tools in artificial intelligence and machine learning research. and more in : NN_f_Article.ipynb