Introduction to Generative AI

Course Title:

Introduction to Generative AI

Course Duration:

3 months (12 weeks)

Frequency:

2 classes per week

Course Content

Week 1: Introduction to Generative AI

• Lecture 1 : What is Generative AI?
  • Overview of AI and Machine Learning.
  • Applications and Use Cases.
  • Tools Used in Generative AI
• Lecture 2 : Descriminative VS Generative Models
  • Generative Models: What are they and why are they important?
  • Exercise: Build a simple text generation model.

Week 2: Understanding Neural Networks

• Lecture 1: Deep Learning and Neural Networks: An Overview
  • Deep Learning Basics and Neural Networks Architecture.
  • Feedforward and Backpropagation.
  • Tools: TensorFlow, Keras.
• Lecture 2: Generative Models in Image Generation: Understanding GANs and VAEs
  • Use Case: How Generative Models are used in image generation (GANs, VAEs).
  • Exercise: Build a simple Neural Network using Keras.

Week 3: Autoencoders

• Lecture 1: Autoencoders: How They Work and Why They Matter
  • Understanding Autoencoders.
  • Different types: Variational Autoencoders (VAEs).
  • Tools: TensorFlow, Keras.
• Lecture 2: Building and Training Autoencoders for Data Compression
  • Use Case: Data Compression and Reconstruction with Autoencoders.
  • Exercise: Build an Autoencoder for image compression.

Week 4: Generative Adversarial Networks (GANs)

• Lecture 1: Introduction to GANs: Theory and Architecture
  • Introduction to GANs: Architecture, Generator, and Discriminator.
  • Real-World Applications of GANs.
  • Tools: PyTorch, TensorFlow.
• Lecture 2: Hands-on: Building a Simple GAN for Image Generation
  • Use Case: Image Generation using GANs.
  • Exercise: Build a basic GAN for generating images.

Week 5: Advanced Generative AI Techniques

• Lecture 1: Overcoming Challenges in GANs: Techniques for Improvement
  • Issues with GANs: Mode Collapse, Training Instabilities.
  • Solutions: Wasserstein GAN, Conditional GAN.
  • Tools: Tensorflow.
• Lecture 2: Building Conditional GANs: A Practical Guide
  • Use Case: Generating specific image categories with Conditional GAN.
  • Exercise: Build a Conditional GAN for a specified dataset (e.g., Fashion MNIST).

Week 6: Sequence Models and Generative Text Models

• Lecture 1: RNNs, LSTMs, and GRUs: Understanding Sequence Models
  • Introduction to Recurrent Neural Networks (RNNs), LSTMs, and GRUs.
  • Sequence-to-Sequence Models and Language Models.
  • Tools: TensorFlow, Keras.
• Lecture 2: Text Generation with RNNs: Hands-on Implementation
  • Use Case: Text Generation and Completion.
  • Exercise: Train an RNN for simple text generation tasks.

Week 7: Transformers and Attention Mechanism

• Lecture 1: Transformers: Revolutionizing AI with Attention Mechanisms
  • Understanding Transformers: Self-Attention and Encoder-Decoder Architecture.
  • From Transformers to GPT (Generative Pretrained Transformer).
  • Tools: Hugging Face Transformers.
• Lecture 2: Implementing Transformers for Language Translation
  • Use Case: Language Translation, Text Summarization.
  • Exercise: Implement a transformer model for text translation.

Week 8: Large Language Models (GPT, BERT)

• Lecture 1: Exploring Large Language Models: GPT and BERT
  • Overview of GPT, BERT, and other large language models.
  • How to fine-tune pretrained models.
  • Tools: Hugging Face, OpenAI API.
• Lecture 2: Fine-tuning GPT for Custom Text Generation Tasks
  • Use Case: Chatbots and Conversational AI.
  • Exercise: Fine-tune GPT for custom text generation tasks.

Week 9: AI Art and Creative Applications

• Lecture 1: Generative AI in Art: From DeepDream to Neural Style Transfer
  • Generative AI in Art: DeepDream, Neural Style Transfer.
  • Tools: Runway ML, Artbreeder, DALL·E.
• Lecture 2: Hands-on: Creating AI-Generated Art with Style Transfer
  • Use Case: Creating AI-generated artwork.
  • Exercise: Use Neural Style Transfer to blend styles between images.

Week 10: Generating Music and Audio

• Lecture 1: Audio and Music Generation with AI: Tools and Techniques
  • Audio Generation with Generative AI: WaveNet, Jukedeck.
  • Tools: Magenta, MuseNet.
• Lecture 2: Hands-on: Building a Music Generator with Magenta
  • Use Case: AI-generated music and sound effects.
  • Exercise: Build a simple music generation model using Magenta.

Week 11: Ethics and Limitations of Generative AI

• Lecture 1: Ethics in Generative AI: Bias, Deepfakes, and Data Privacy
  • Ethical concerns in Generative AI: Deepfakes, Bias, Data Privacy.
  • Tools: OpenAI API, AI Ethics frameworks.
• Lecture 2: Case Study: Assessing Ethical Implications of Generative Models
  • Use Case: Assessing bias in generative models.
  • Exercise: Analyze the ethical implications of a given generative AI model.

Week 12: Capstone Project and Deployment

• Lecture 1: Deploying Generative AI Models: Tools and Best Practices
  • How to deploy generative AI models.
  • Tools: Docker, AWS, TensorFlow Serving.
• Lecture 2: Capstone Project: Building and Deploying Your Own Generative Model
  • Capstone Project: Students create and deploy their own generative model (e.g., an image generator or text generator).
  • Review and Final Q&A.