Why this course?

Description

structured approach to documenting internship experiences and capstone projects focused on Generative AI. Spanning foundational deep learning to advanced generative architectures like GANs, VAEs, and LLMs, it guides learners in crafting impactful projects and professional-grade reports.

Objectives

• After completing this chapter, learners will be able to:
• Develop and evaluate deep learning models using PyTorch.
• Apply Generative AI models such as GANs, VAEs, and LLMs for tasks like image generation and text creation.
• Handle data preprocessing, model training, and result interpretation for generative tasks.
• Create a detailed and structured internship or capstone report.

Section 1: Generative AI Project Highlights

Project 1: Breast Cancer Prediction using ANN

Objective: Use a basic artificial neural network to predict breast cancer from clinical data.

Project 2: Flight Fare Prediction using ANN

Objective: Build a regression model to predict flight prices.

Project 3: Leaf Disease Detection using CNN

Objective: Classify plant leaf images using convolutional neural networks.

Project 4: MNIST Digits and Fashion Image Generation

Objective: Generate new digit and fashion images using GAN and VAE.

Project 5: Image Translation with GANs

Objective: Perform image-to-image translation using models like CycleGAN and Pix2Pix.

Project 6: High-Resolution Face Generation

Objective: Use advanced GANs (e.g., ProGAN, SRGAN) to generate photorealistic faces.

Project 7: Conditional Image Generation

Objective: Apply CGAN to generate images conditioned on labels (e.g., MNIST digits).

Project 8: LLM-based Applications

Objective: Build interactive tools such as:

• Chat with PDF using LLAMA2
• Blog and SQL generation
• YouTube video summarization using Google Gemini

Section 2: Final Generative AI Internship Report Structure

Title Page

• Project name
• Student and mentor details
• Institute and internship dates

Abstract

• One-paragraph summary of goals and outcomes

Introduction

• Background on Generative AI and relevance to the domain

Tools and Dataset

• List of datasets used (MNIST, CIFAR-10, CelebA, etc.)
• Libraries and frameworks (PyTorch, TorchVision, etc.)

Model Design and Training

• Description of model architecture (ANN, CNN, GAN, VAE, LLM)
• Training procedures, epochs, optimizers

Results and Analysis

• Performance evaluation (loss curves, sample outputs)
• Comparison between models (if applicable)

Challenges and Learnings

• Technical challenges faced
• Insights gained during development

Conclusion and Future Work

• Key takeaways
• Suggested enhancements or deployment ideas

References

• Papers, frameworks, tutorials cited

Appendices

• Screenshots, sample code, model weights link