Style Transfer with Neural Network (#441)

## Pull Request for PyVerse 💡

### Requesting to submit a pull request to the PyVerse repository.

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#### Issue Title
**Please enter the title of the issue related to your pull request.**  
*Style Transfer with Neural Network*

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#### Info about the Related Issue
**What's the goal of the project?**  
*This project aims to implement a style transfer technique using neural
networks to blend content and style images, allowing users to generate
artistic images by combining different visual elements.*

- [x] I have described the aim of the project.

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#### Name
**Please mention your name.**  
*Alolika Bhowmik*

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#### GitHub ID
**Please mention your GitHub ID.**  
*alo7lika*

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#### Email ID
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*alolikabhowmik72@gmail.com*

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#### Identify Yourself
**Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC,
SWOC).**
*GSSOC*

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#### Closes
**Enter the issue number that will be closed through this PR.**  #405 
*Closes: 

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#### Describe the Add-ons or Changes You've Made
**Give a clear description of what you have added or modified.**  
*Implemented a Jupyter Notebook that demonstrates style transfer using
PyTorch. Added sample images, updated documentation, and included a
requirements.txt file for dependencies.*

- [x] I have described my changes.

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#### Type of Change
**Select the type of change:**  
- [ ] Bug fix (non-breaking change which fixes an issue)
- [x] New feature (non-breaking change which adds functionality)
- [ ] Code style update (formatting, local variables)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] This change requires a documentation update

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#### How Has This Been Tested?
**Describe how your changes have been tested.**  
*Tested the style transfer implementation with various content and style
images, ensuring that the output is generated correctly and meets
expectations.*

- [x] I have described my testing process.

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#### Checklist
**Please confirm the following:**  
- [x] My code follows the guidelines of this project.
- [x] I have performed a self-review of my own code.
- [x] I have commented my code, particularly wherever it was hard to
understand.
- [x] I have made corresponding changes to the documentation.
- [x] My changes generate no new warnings.
- [x] I have added things that prove my fix is effective or that my
feature works.
- [x] Any dependent changes have been merged and published in downstream
modules.

Machine_Learning/Style Transfer with Neural Network/README.md

@@ -0,0 +1,103 @@
+# 🎨 Style Transfer with Neural Networks 🖼️
+
+Welcome to the **Style Transfer with Neural Networks** project! In this project, we explore the application of neural networks to perform artistic style transfer, allowing us to blend the artistic style of one image with the content of another. 
+
+## Table of Content
+
+1. [Project Overview](#-project-overview)
+2. [Setup & Installation](#-setup--installation)
+3. [File Structure](#-file-structure)
+4. [How to Run](#-how-to-run)
+5. [Sample Run](#-sample-run)
+6. [Concepts Behind the Project](#-concepts-behind-the-project)
+7. [Examples of Generated Images](#-examples-of-generated-images)
+8. [Technologies Used](#-technologies-used)
+9. [Parameters & Tuning](#-parameters--tuning)
+10. [License](#-license)
+11. [Contact](#-contact)
+
+## 📚 Project Overview
+
+Style transfer is a technique in deep learning that involves using Convolutional Neural Networks (CNNs) to separate and combine the style of one image with the content of another image. This project implements a neural style transfer model using **PyTorch** and **pre-trained VGG19**.
+
+### Key Features:
+- Use of **pre-trained neural networks** for feature extraction (VGG19)
+- Content loss and style loss based on **Gram matrices**
+- Supports custom content and style images
+- Optimization with **L-BFGS** optimizer for faster convergence
+
+## 🛠️ Setup & Installation
+
+To get started with this project, you'll need to install the following dependencies:
+
+```bash
+pip install torch torchvision matplotlib Pillow
+```
+
+### File Structure:
+
+| File/Folder                           | Description                                           |
+|---------------------------------------|-------------------------------------------------------|
+| `Style Transfer with Neural Networks.ipynb` | The main notebook implementing style transfer        |
+| `images/`                             | Directory containing sample content and style images  |
+| `output/`                             | Folder to store the generated images                  |
+| `requirements.txt`                    | List of dependencies                                  |
+
+## 🚀 How to Run
+
+1. **Clone the repository** and navigate to the project folder:
+
+   ```bash
+   git clone https://github.com/yourusername/style-transfer-project.git
+   cd style-transfer-project
+   ```
+2. **Run the Jupyter Notebook** to perform style transfer on your custom images. You can upload your content and style images directly in the notebook.
+
+3. Adjust parameters like `content_weight`, `style_weight`, and `num_steps` to tweak the results according to your preferences.
+
+4. View and save the generated images in the `output/` folder.
+
+## 📸 Sample Run
+
+- **Content Image**: Defines the structure and objects in the final image.
+- **Style Image**: Provides the textures, colors, and patterns.
+
+| Content Image                                                                                  | Style Image                                                                                   |
+|------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
+| ![Content Image](https://raw.githubusercontent.com/alo7lika/PyVerse/refs/heads/main/Machine_Learning/Style%20Transfer%20with%20Neural%20Network/content%20image.jpg) | ![Style Image](https://raw.githubusercontent.com/alo7lika/PyVerse/refs/heads/main/Machine_Learning/Style%20Transfer%20with%20Neural%20Network/style%20image.jpg) |
+
+
+## 🔬 Concepts Behind the Project
+
+- **Content Representation**: Extracted from deeper layers of the network to capture high-level structures in the image.
+
+- **Style Representation**: Captured using the **Gram matrix** of feature maps, representing correlations between different feature maps.
+
+- **Optimization**: The neural network optimizes a noise image to minimize both **content loss** and **style loss**, blending the content and style.
+
+## 🧠 Technologies Used
+
+- **Python** 🐍
+- **PyTorch** for deep learning
+- **Jupyter Notebook** for interactive coding
+- **Matplotlib** for visualizations
+
+## 📊 Parameters & Tuning
+
+You can adjust the following parameters to control the output of the style transfer model:
+
+| Parameter       | Default Value | Description                         |
+|-----------------|---------------|-------------------------------------|
+| `content_weight` | `1e5`         | Weight for the content loss         |
+| `style_weight`   | `1e10`        | Weight for the style loss           |
+| `num_steps`      | `300`         | Number of optimization steps        |
+| `learning_rate`  | `0.01`        | Learning rate for the optimizer     |
+
+
+## 📄 License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## 💬 Contact
+
+If you have any questions, feel free to reach out to me at [alolikabhowmik72@gmail.com]