Neural Style Transfer Project Added (new folder)

Neural Style Transfer/how-to-use.md

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+# Neural Style Transfer
+
+## Table of Contents
+1. [Introduction](#introduction)
+2. [Project Structure](#project-structure)
+3. [Installation](#installation)
+4. [How It Works](#how-it-works)
+5. [Future Enhancements](#future-enhancements)
+
+---
+
+### Introduction
+
+**Neural Style Transfer** is a project that applies the artistic style of one image (style image) to another image (content image) using deep learning techniques. For this project, I have chosen a pre-trained model from TensorFlow Hub, which allows for efficient and effective style transfer. The project leverages this model to create new images by merging the content of one image with the style of another, enabling users to transform their photos into unique artistic expressions.
+
+- Pretrained Model - https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2
+---
+
+### Project Structure
+
+```bash
+NeuralStyleTransfer/
+│
+├── images/                 # Folder containing input content images
+│   ├── rabit.jpg
+│   └── city.jpg
+│   └── ...
+│
+├── styles/                 # Folder containing input style images
+│   ├── style1.jpg
+│   └── ...
+│
+├── generated/              # Folder for storing generated styled images
+│   ├── generated_image_{}.jpg
+│   └── ...
+│
+├── requirements.txt                            # List of dependencies
+├── how-to-use.md                               # Project documentation (this file)
+└── Neural_Style_Transfer.ipynb                 # Main script to run the neural style transfer
+```
+--- 
+
+### Installation
+To run the project locally, follow these steps:
+1. Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+2. Fork and Clone the repository:
+```bash
+git clone https://github.com/username/machine-learning-repos.git
+cd Neural Style Transfer
+```
+
+--- 
+
+### How It Works
+The Neural Style Transfer algorithm works by combining the content of one image with the style of another using a deep neural network. It minimizes the differences between:
+
+1. The content features of the generated image and the content image.
+2. The style features of the generated image and the style image.
+
+- Content Image: Preserves the structure and details.
+- Style Image: Adds artistic characteristics, such as brush strokes and color patterns.
+- Generated Image: The final result that merges content and style.
+
+---
+### Sample Output
+![PNG image](https://github.com/user-attachments/assets/acbe6b29-5088-4346-8433-216efa604418)
+
+---
+### Future Enhancements
+Here are some potential improvements for future releases:
+
+- Implement a custom CNN model for a comprehensive output.
+- Add more advanced style transfer techniques for real-time processing.
+- Enhance the UI or add a GUI for easier use.
+
+---