Deep Learning Basics

• Introduction to Neural Networks
• Activation Functions
• Backpropagation
• Gradient Descent
• Overfitting and Regularization

Convolutional Neural Networks (CNN)

• Image Processing Basics
• Filters and Feature Maps
• Pooling Layers
• Transfer Learning
• Object Detection Models

Recurrent Neural Networks (RNN)

• Sequence Modeling
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRU)
• Time Series Forecasting
• Text Processing with RNN

Generative Models

• Introduction to Generative AI
• Autoencoders
• Variational Autoencoders (VAE)
• Generative Adversarial Networks (GANs)
• Diffusion Models

Large Language Models (LLMs)

• Understanding Transformers
• BERT and GPT Architecture
• Fine-tuning Pre-trained Models
• Prompt Engineering
• Applications of LLMs

Advanced Topics

• Multimodal AI (Text-to-Image)
• Ethics in Generative AI
• Bias and Fairness
• Deploying AI Models
• Real-Time Generative AI Systems

Mini Projects

• AI-Powered Chatbot
• Image Style Transfer
• Text Summarizer
• Voice-to-Text System
• Music Generation

Capstone Project

Project Name: AI Content Generation Platform

• Data Collection and Preprocessing
• Model Training and Fine-Tuning
• Frontend Development for User Interaction
• Backend APIs for Model Integration
• Cloud Deployment

AI Development Tools

• TensorFlow and PyTorch
• Hugging Face Transformers
• OpenAI APIs
• Google Colab and Jupyter Notebooks
• Docker and Kubernetes

Deployment and Integration

• FastAPI and Flask for APIs
• Cloud Services (AWS, GCP, Azure)
• Version Control with Git
• CI/CD Pipelines
• Monitoring and Scaling AI Systems