Finest Generative AI Course in Pune | Generative AI Certification in Pune

Module 1: Introduction to Generative AI

Overview of Artificial Intelligence and Machine Learning

What is Generative AI? Definition and scope

Difference between Generative AI and Discriminative AI

Real-world applications (e.g., content creation, art, drug discovery)

Course roadmap and expectations

Module 2: Basics of Machine Learning

Definition: Machine Learning is teaching computers to learn from data and make predictions or decisions.

Types of Learning:

Supervised Learning: Uses labeled data (e.g., predicting house prices with past sales data).

Unsupervised Learning: Finds patterns without labels (e.g., grouping customers by behavior).

Key Algorithms:

Linear Regression: Predicts numbers (e.g., sales).

Decision Trees: Makes decisions by splitting data into branches.

K-Means Clustering: Groups similar data points together.

Common Problems:

Overfitting: Model learns training data too well, fails on new data.

Underfitting: Model is too simple, misses patterns.

Fix with regularization (e.g., adding penalties to complex models).

Practical Work: Install Python, set up Jupyter Notebook, and use NumPy (for math) and Pandas (for data handling).

Module 3: Introduction to Deep Learning

Neural Networks: Systems of connected nodes (neurons) in layers that process data.

Input Layer: Takes data.

Hidden Layers: Process data.

Output Layer: Gives results.

Uses activation functions (e.g., ReLU) to decide what passes through.

Training Process:

Backpropagation: Adjusts weights by calculating errors backward.

Gradient Descent: Updates weights to reduce errors step-by-step.

Loss Functions: Measures error (e.g., Mean Squared Error for numbers).

Tools:

TensorFlow: Library for building and training neural networks.

PyTorch: Another library, popular for flexibility.

Practical Work: Write code to build a basic neural network in Python using TensorFlow or PyTorch.

Module 4: Probability and Statistics for Generative AI

Probability Basics:

Distributions: Normal (bell-shaped), Binomial (yes/no outcomes).

Used to model randomness in data.

Bayes’ Theorem: Calculates probabilities based on conditions (e.g., probability of rain given wet streets).

Key Concepts:

Expectation: Average value you expect from data.

Variance: How spread out data is.

Covariance: How two variables change together.

Sampling: Picking random data points to estimate patterns.

Monte Carlo Method: Uses random samples to solve problems (e.g., approximating results).

Practical Work: Use Python to generate random data (e.g., coin flips, normal distribution) and analyze it.

Module 5: Introduction to Generative Models

Generative Models

Types of Generative Models

Comparison with Discriminative Models

Key challenges in Generative AI

Case Study: Early generative model examples

Module 6: Autoencoders

Architecture of Autoencoders (Encoder-Decoder)

Training Autoencoders for Data Reconstruction

Module 7: Variational Autoencoders

Introduction to VAEs and Latent Space

KL Divergence and Reparameterization Trick

Generating New Data with VAEs

Use Cases: Image Generation, Data Imputation

Module 8: Generative Adversarial Networks Part 1

GAN Architecture: Generator vs. Discriminator

Adversarial Training Process

Loss Functions in GANs (Minimax Loss)

Challenges: Mode Collapse, Training Instability

Module 9: Generative Adversarial Networks Part 2

Advanced GAN Variants

Conditional GANs for Controlled Generation

Evaluating GAN Performance (Inception Score, FID)

Hands-on: Training a DCGAN for image synthesis

Module 10: Autoregressive Models

What are Autoregressive Models?

PixelRNN and PixelCNN: Architecture and Training

Applications in Text and Image Generation

Pros and Cons vs. GANs and VAEs

Hands-on: Implementing a simple autoregressive model

Module 11: Diffusion Models - Part 1

Introduction to Diffusion Processes

Forward & Reverse Diffusion

DDPM

GANs and VAEs

Hands-on: Exploring diffusion basics

Module 12: Diffusion Models - Part 2

Advanced Diffusion Techniques (Score-based Models)

Applications: High-Quality Image Generation

Optimizing Diffusion Models

Hands-on: Building a diffusion model for image generation

Module 13: Transformers in Generative AI

Introduction to Transformer Architecture

Attention Mechanisms and Self-Attention

NLP and Vision

GPTs

Tuning a GPT model

Module 14: Text Generation with Generative AI

Language Models: RNNs, LSTMs, and Transformers

Techniques for Text Generation (Beam Search, Sampling)

Evaluating Text Quality (BLEU, ROUGE)

Applications: Chatbots, Story Generation

Hands-on: Generating text with a pre-trained model

Module 15: Image Generation Techniques

GANs and VAEs, & Diffusion Models

Style Transfer & from Image to another Img Translation

Super-Resolution with Generative Models

Hands-on: Creating a style transfer application

Module 16: Audio Generation with Generative AI

Introduction to Audio Data and Spectrograms

WaveNet and SampleRNN

Generating Music and Speech

Challenges in Audio Synthesis

Hands-on: Generating simple audio samples

Module 17: Multimodal Generative Models

Combining Text, Image, and Audio Data

CLIP and DALL·E: Text-to-Image Generation

Cross-Modal Learning Challenges

Hands-on: Building a text-to-image pipeline

Module 18: Ethics in Generative AI

Bias and Fairness in Generative Models

Deepfakes and Misinformation Risks

Intellectual Property and Copyright Issues

Responsible AI Development Practices

Case Study: Ethical dilemmas in AI deployment

Module 19: Data Preparation for Generative AI

Data Collection and Cleaning

Augmentation Techniques for Small Datasets

Handling Imbalanced Data

Preprocessing for Images, Text, and Audio

Hands-on: Preparing a dataset for training

Module 20: Training and Fine-Tuning Generative Models

Hyperparameter Tuning (Learning Rate, Batch Size)

Transfer Learning in Generative AI

Debugging Training Issues

Hands-on: Fine-tuning a pre-trained GAN

Module 21: Evaluation Metrics for Generative Models

Quantitative Metrics (FID, IS, Perplexity)

Qualitative Evaluating Techniques

Human in Loop Evaluating

Hands-on: Evaluating a generative model’s output

Module 22: Optimization Techniques

Advanced Optimizers (Adam, RMSprop)

Gradient Clipping and Weight Initialization

Stabilizing GAN Training

Hands-on: Optimizing a generative model

Module 23: Generative AI in Industry - Part 1

Healthcare: Drug Discovery and Medical Imaging

Entertainment: Game Design and CGI

Marketing: Content Creation and Personalization

Case Studies: Industry Success Stories

Module 24: Generative AI in Industry - Part 2

Finance: Synthetic Data Generation

Manufacturing: Design Optimization

Retail: Product Visualization

Hands-on: Solving an industry-specific problem

Module 25: Deployment of Generative Models

Model Compression and Efficiency

Serving Models with APIs (Flask, FastAPI)

Cloud Deployment (AWS, GCP, Azure)

Hands-on: Deploying a generative model online

Module 26: Scaling Generative AI Solutions

Distributed Training with Multiple GPUs

Handling Large-Scale Datasets

Real-Time Generative Applications

Hands-on: Scaling a model with cloud resources

Module 27: Security in Generative AI

Adversarial Attacks on Generative Models

Protecting Models from Data Poisoning

Secure Model Deployment

Hands-on: Simulating an adversarial attack

Module 28: Future Trends in Generative AI

Emerging Models and Techniques

Quantum Computing and Generative AI

AI Democratization and Open-Source Tools

Discussion: Predicting the next big breakthrough

Module 29: Capstone Project - Part 1

Project Planning and Proposal

Choosing a Generative AI Application

Data Collection and Model Selection

Milestone: Submit project outline

Module 30: Capstone Project - Part 2

Implementation and Testing

Presentation of Results

Peer Review and Feedback

Final Submission and Certification