MLSAKIET_GENAI_INTERNSHIP

Welcome to the Generative AI Internship repository provided by MLSA KIET! This repository is designed to provide a comprehensive learning experience in the field of Generative Artificial Intelligence. Throughout this internship, you will work on various projects, gain hands-on experience with cutting-edge technologies, and develop a deep understanding of generative models.

Table of Contents

1. Introduction
2. Objectives
3. Sample Projects
4. Requirements
5. Pull Request
6. Contact

Introduction

Welcome to the Generative AI Internship! This program is designed to provide hands-on experience with cutting-edge generative AI technologies. Interns will work on real-world projects, develop advanced machine learning models, and gain a deep understanding of generative algorithms such as GANs, VAEs, and autoregressive models. Throughout the internship, participants will have the opportunity to collaborate with experienced AI researchers and industry professionals, enhancing both their technical skills and professional network. Whether you're looking to start a career in AI or expand your existing knowledge, this internship offers a comprehensive learning experience in the exciting field of generative AI. We look forward to your contributions and are excited to see the innovative projects you'll develop!

Objectives

Some of the basic objective of the intenship is to provide:

1. Practical Proficiency: Equip students with practical skills in utilizing sophisticated generative AI tools and frameworks.
2. Problem-Solving Enhancement: Strengthen students' problem-solving capabilities and technical expertise in AI.
3. Competitive Readiness: Prepare students for competitive scenarios in the tech industry.
4. Technological Understanding: Foster a deep understanding of the latest technologies and their applications.
5. Git/GitHub Proficiency: Teach students how to control their repository and work in collaborations using Git/GitHub.
6. Project Development Workflow: Ensure students learn the complete workflow of engineering project development and management.

Rules and Regulations

1. Eligibility: Open to all first-year and second-year students of KIET Group of Institutions.
2. Attendance: Students must attend all scheduled sessions and complete all assigned tasks.
3. Assignments: All assignments and projects must be submitted on time.
4. Ethics: Students must adhere to the highest standards of academic integrity and professionalism.
5. Collaboration: While collaboration is encouraged, all work submitted must be original and completed by the individual.

Sample Projects

During this internship, you can work on the following projects or can have an idea to develop the projects which can be derived from this:

1. Basic GAN Implementation

Understand the architecture of GANs and implement a basic GAN to generate images.

2. Variational Autoencoder

Learn about VAEs and build a model to generate new data points from learned representations.

3. Text Generation with GPT

Use GPT (Generative Pre-trained Transformer) models for text generation tasks.

4. Style Transfer

Implement neural style transfer to apply artistic styles to images.

5. Music Generation

Generate music using generative models like WaveNet.

Requirements

To get started with the projects, ensure you have the following prerequisites: -

1. Latest Vesion of Python

2. Basic knowledge of TensorFlow, PyTorch and Pandas

3. Use of IDE’s like Jupyter Notebook, VS Code, PyCharm, etc

4. Basic understanding of Machine Learning and Deep Learning

Pull Request

Making a pull request on GitHub involves several steps, starting from cloning a repository, making changes, and then submitting the pull request. Here's a step-by-step guide on how to do this with code for your contribution:

1. Fork the Repository

First, fork the repository you want to contribute to. This will create a copy of the repository in your GitHub account.

2. Clone the Forked Repository

Clone the forked repository to your local machine using the following command:

git clone https://github.com/your-username/repository-name.git

cd repository-name

Replace your-username with your GitHub username and repository-name with the name of

the repository you forked.

3. Create a New Branch

Create a new branch for your changes. It's a good practice to name your branch based on the

feature or fix you are working on:

git checkout -b feature-branch-name

Replace feature-branch-name with a meaningful name for your branch.

4. Make Your Changes

Make the necessary changes to the codebase using your preferred code editor or IDE.

5. Stage and Commit Your Changes

After making your changes, stage the changes using:

git add .

Then commit your changes with a meaningful commit message:

git commit -m "Description of the changes"

6. Push Your Changes to GitHub

Push your changes to your forked repository on GitHub:

git push origin feature-branch-name

7. Create a Pull Request

1. Go to your forked repository on GitHub.

2. You should see a notification to compare & pull requests. Click on "Compare & pull request".

3. Ensure the base repository is the original repository you forked from, and the base branch is

the branch you want to merge into (usually main or master).

4. Ensure the head repository is your forked repository, and the compare branch is the branch

you just pushed.

5. Add a title and description for your pull request.

6. Click "Create pull request".

That's it! You've successfully created a pull request on GitHub. The repository maintainers

will review your changes and merge them if everything looks good.

Please ensure your contributions adhere to our Code of Conduct.

Contact

Utkarsh Jain (Outreach Head, MLSA KIET])

utkarsh.2125cse1179@kiet.edu

Srishti Upadhyay (Technical Head, MLSA KIET)

srishti.2125cse1048@kiet.edu

Happy learning and Innovating!