BE-SEM-8 🎓

Welcome to the BE SEM 8 Assignments repository! This repository houses various assignments related to the 8th semester of the Bachelor of Engineering program. Here, you will find projects that cover a range of topics, including deep learning, high-performance computing (HPC), and data analysis using popular libraries like TensorFlow, Keras, and more.

Table of Contents

• Introduction
• Topics Covered
• Installation
• Usage
• Contributing
• License
• Releases
• Contact

Introduction

This repository serves as a learning platform for students enrolled in the Bachelor of Engineering program. The assignments focus on practical applications of theoretical concepts learned throughout the semester. Each project aims to enhance understanding and foster skills in programming and data science.

Topics Covered

The assignments in this repository cover the following topics:

• C++: A powerful programming language for system/software development.
• Deep Learning: Techniques for training neural networks and understanding complex data.
• High-Performance Computing (HPC): Methods to solve complex problems using parallel processing.
• Keras: A high-level neural networks API for building and training models.
• Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.
• NumPy: A library for numerical computing in Python, providing support for arrays and matrices.
• OpenMP: An API for multi-platform shared-memory parallel programming in C, C++, and Fortran.
• Pandas: A library providing data structures and data analysis tools for Python.
• Scikit-learn: A library for machine learning in Python, offering simple and efficient tools.
• Seaborn: A statistical data visualization library based on Matplotlib.
• TensorFlow: An end-to-end open-source platform for machine learning.

Installation

To set up the environment for running the assignments, follow these steps:

1. Clone the repository:

   git clone https://github.com/yajuop/BE-SEM-8.git
   cd BE-SEM-8

2. Install required libraries: It is recommended to use a virtual environment. You can create one using venv or conda.

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

3. Check for additional setup: Some assignments may require specific setup instructions. Refer to the individual assignment directories for more details.

Usage

Each assignment is structured to allow easy execution. Navigate to the specific assignment directory and follow the instructions in the README file located there. You can also find example datasets and scripts to help you get started.

Example

To run an assignment:

1. Navigate to the assignment directory:

   cd assignment_name

2. Execute the main script:

   python main.py

3. Visualize results using Matplotlib or Seaborn as needed.

Contributing

Contributions are welcome! If you have suggestions or improvements, feel free to fork the repository and submit a pull request. Please ensure that your code adheres to the existing style and includes comments where necessary.

1. Fork the repository.
2. Create your feature branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -m "Add some feature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Releases

For the latest releases, visit the Releases section. Download the files and execute them as instructed in the respective directories. This will help you access the latest updates and features added to the assignments.

Contact

For any inquiries or feedback, feel free to reach out:

• Email: your-email@example.com
• LinkedIn: Your LinkedIn Profile

---

Feel free to explore the repository and utilize the resources provided. Happy coding!