AI

Section 1: Introduction to Generative AI

Duration:1 week

Topics:

• What is generative AI? History and significance
• Generative vs. discriminative models
• Key generative AI techniques: GANs, VAEs, diffusion models, LLMs
• Applications: Image generation, text generation, music synthesis
• Ethical considerations: Bias, misinformation, and responsible AI

Learning Outcomes:

• Define generative AI and its key techniques
• Identify real-world applications and ethical challenges

Activities:

• Case study: Analyze a generative AI application (e.g., DALL·E, ChatGPT)
• Discussion: Ethical implications of AI-generated content

Resources:

• Articles: Generative AI from MIT Technology Review, Nature
• Videos: TED Talks on AI creativity

Section 2: Python and Data Preparation for Generative AI

Duration:2 weeks

Topics:

– Week 1:Python Review and Setup

• Setting up Python (Anaconda, Jupyter Notebook)
• Review of NumPy and Pandas for data handling
• Introduction to PyTorch for generative models
• Basic data visualization with Matplotlib

– Week 2:SQL and Data Preprocessing

• Introduction to MySQL for data extraction
• Writing SELECT queries, JOINs, and aggregations
• Preprocessing datasets: Normalization, augmentation, and cleaning
• Preparing image and text datasets for generative models

Learning Outcomes:

• Perform data manipulation and preprocessing with Python
• Query and prepare datasets using SQL

Activities:

• Hands-on: Clean an image dataset with Pandas
• Exercise: Query a text dataset with SQL

Resources:

• Anaconda, Jupyter Notebook, MySQL Workbench
• Libraries: NumPy, Pandas, PyTorch, Matplotlib
• Sample datasets: MNIST, IMDb reviews

Section 3: Foundations of Machine Learning for Generative AI

Duration:2 weeks

Topics:

– Week 1:Machine Learning Basics

• Overview of supervised and unsupervised learning
• Neural networks: Neurons, layers, activation functions
• Loss functions and optimization (e.g., gradient descent)
• Introduction to Scikit-learn and PyTorch

– Week 2:Probabilistic Models

• Probability distributions: Gaussian, Bernoulli
• Maximum likelihood estimation
• Introduction to latent variables and Bayesian inference
• Preparing for VAEs and diffusion models

Learning Outcomes:

• Understand neural network basics
• Apply probabilistic concepts to generative models

Activities:

• Hands-on: Build a simple neural network with PyTorch
• Exercise: Fit a Gaussian distribution to data

Resources:

• Scikit-learn, PyTorch
• Sample datasets: Synthetic data, Iris

Section 4: Variational Autoencoders (VAEs)

Duration:2 weeks

Topics:

– Week 1:VAE Fundamentals

• Autoencoders: Encoder-decoder architecture
• VAEs: Latent space and KL-divergence
• Building a VAE with PyTorch
• Loss function: Reconstruction loss + regularization

– Week 2:VAE Applications

• Generating images with VAEs
• Interpolating in latent space
• Evaluating VAE performance: Visual inspection, log-likelihood
• Limitations and challenges of VAEs

Learning Outcomes:

• Build and train VAEs for image generation
• Understand latent space representations

Activities:

• Hands-on: Train a VAE on MNIST digits
• Project: Generate new digit images

Resources:

• PyTorch
• Sample dataset: MNIST

Section 5: Generative Adversarial Networks (GANs)

Duration:2 weeks

Topics:

– Week 1:GAN Fundamentals

• GAN architecture: Generator vs. discriminator
• Training GANs: Min-max game, loss functions
• Building a simple GAN with PyTorch
• Challenges: Mode collapse, training instability

– Week 2:Advanced GANs

• Deep Convolutional GANs (DCGANs)
• Conditional GANs for controlled generation
• Evaluating GANs: FID score, visual quality
• Applications: Image synthesis, style transfer

Learning Outcomes:

• Build and train GANs for image generation
• Apply advanced GAN techniques

Activities:

• Hands-on: Train a GAN on CIFAR-10
• Project: Generate realistic faces with a DCGAN

Resources:

• PyTorch
• Sample datasets: CIFAR-10, CelebA

Section 6: Diffusion Models

Duration:2 weeks

Topics:

– Week 1:Diffusion Model Basics

• What are diffusion models? Forward and reverse processes
• Denoising diffusion probabilistic models (DDPM)
• Building a simple diffusion model with PyTorch
• Training and sampling processes

– Week 2:Applications and Advances

• Generating high-quality images with diffusion models
• Comparing diffusion models to GANs and VAEs
• Exploring stable diffusion and its applications
• Limitations: Computational cost, sampling time

Learning Outcomes:

• Understand and implement diffusion models
• Generate images using diffusion techniques

Activities:

• Hands-on: Train a diffusion model on MNIST
• Project: Experiment with stable diffusion for image generation

Resources:

• PyTorch, Hugging Face Diffusers
• Sample dataset: MNIST

Section 7: Introduction to Large Language Models (LLMs)

Duration:2 weeks

Topics:

– Week 1:LLM Fundamentals

• What are LLMs? Transformers and attention mechanisms
• Pre-trained models: BERT, GPT, LLaMA
• Fine-tuning LLMs with Hugging Face
• Text generation and prompt engineering

– Week 2:LLM Applications

• Building a chatbot with a pre-trained LLM
• Text summarization and sentiment analysis
• Ethical considerations: Bias, energy consumption
• Evaluating LLMs: Perplexity, human evaluation

Learning Outcomes:

• Use pre-trained LLMs for text generation
• Fine-tune LLMs for specific tasks

Activities:

• Hands-on: Generate text with GPT-2
• Project: Build a simple chatbot

Resources:

• Hugging Face Transformers, PyTorch
• Sample dataset: WikiText

Section 8: Capstone Project

Duration:2 weeks

Objective:Apply generative AI techniques to create a novel AI-generated output

Project Examples:

• Generate synthetic artwork using a GAN or diffusion model
• Create a text-generating chatbot for a specific domain
• Produce a dataset of AI-generated images for a niche application

Deliverables:

• SQL queries to prepare data (if applicable)
• Python scripts for model training and generation
• Generated outputs (images, text, etc.)
• Report summarizing methodology and results

Learning Outcomes:

• Synthesize generative AI skills in a practical project
• Present and evaluate AI-generated outputs

Resources:

• Kaggle datasets, Hugging Face datasets
• PyTorch, Hugging Face libraries

Section 9: Career Preparation and Next Steps

Duration:1 week

Topics:

• Building a generative AI portfolio
• Resume and LinkedIn optimization for AI roles
• Preparing for generative AI interviews (coding, concepts)
• Overview of advanced topics: Reinforcement learning, multimodal models
• Certifications: Google Professional Machine Learning Engineer

Learning Outcomes:

• Create a professional portfolio with generative AI projects
• Prepare for AI job applications

Activities:

• Build a portfolio with capstone project
• Mock interview with Python and generative AI questions

Resources:

• Kaggle, GitHub for portfolio hosting
• Free tutorials: Hugging Face, PyTorch

Course Duration

• Total: 16 weeks (assuming 10-15 hours per week)
• Format: Self-paced with optional instructor-led sessions

Prerequisites

• Basic Python programming (e.g., familiarity with loops, functions)
• Basic understanding of machine learning (e.g., neural networks)
• No prior generative AI experience required
• Access to a PC with internet for tool installations

Tools and Software

• Python: Anaconda, Jupyter Notebook (free)
• SQL: MySQL Workbench (free)
• Libraries: NumPy, Pandas, PyTorch, Hugging Face Transformers, Matplotlib
• Others: Google Colab (free, web-based, GPU access)
• Git: Version control (free)

Recommended Resources

• Books: “Deep Learning” by Ian Goodfellow et al., “Generative Deep Learning” by David Foster
• Online platforms: Coursera, Udemy, Kaggle, Hugging Face
• Free Tutorials: PyTorch.org, HuggingFace.co
• Datasets: Kaggle, UCI Repository, Hugging Face Datasets

Certification Preparation

• Google Professional Machine Learning Engineer
• Coursera’s “Generative AI with LLMs” (DeepLearning.AI)
• Hugging Face Generative AI courses (if available)