30 Days Internship on Generative AI

Day 1: Introduction to AI, ML, and DL
Day 2: Python for Machine Learning
Day 3: Linear Algebra and Calculus for ML
Day 4: Supervised and Unsupervised Learning
Day 5: Model Evaluation and Cross-Validation
Day 6: Introduction to Neural Networks
Day 7: Convolutional Neural Networks (CNNs)
Day 8: Recurrent Neural Networks (RNNs)
Day 9: LSTM and GRU Networks
Day 10: Autoencoders and Variational Autoencoders (VAEs)
Day 11: Introduction to GANs
Day 12: Deep Convolutional GAN (DCGAN)
Day 13: Wasserstein GAN (WGAN)
Day 14: Conditional GANs (cGANs)
Day 15: CycleGAN
Day 16: Introduction to Language Models
Day 17: Transformer Architecture
Day 18: Hugging Face and Pre-trained Models
Day 19: Text Summarization and Question Answering
Day 20: Machine Translation
Day 21: Introduction to Retrieval-Augmented Generation (RAG)
Day 22: LangChain for LLMs
Day 23: Few-shot and Zero-shot Learning
Day 24: Prompt Engineering
Day 25: Advanced Fine-Tuning Techniques
Day 26: Generative Models for Healthcare Applications
Day 27: Generative Models for Scientific Applications
Day 28: Generative Models for Art and Design
Day 29: Generative Models for Audio and Time Series
Day 30: Real-world Final Project