Generative AI Tutorial

Overview
Curriculum
Instructor

Here we’ll learn:

Generative AI – Home

FAQs on Generative AI

Basics of Generative AI

Evolution of Generative AI

ML and Generative AI

Discriminative vs Generative Models

Types of Generative Models

The Role of Probability Distribution in Generative Models

The Role of Probability Density Functions in Generative AI Models

Generative AI Models – Maximum Likelihood Estimation

MLE in Generative Modeling

How do Generative Adversarial Networks (GAN) Work?

Generative Adversarial Network – Architecture and Types

Conditional Generative Adversarial Networks (cGAN)

CycleGAN and StyleGAN

Training a Generative Adversarial Network (GANs)

Generative Adversarial Networks Applications

Transformers in Generative AI

Architecture of Transformers in Generative AI

Input Embeddings in Transformers

Multi-Head Attention in Transformers

Positional Encoding in Transformer Models

Feed Forward Neural Network in Transformers

Normalization and Residual Connections

Autoencoders in Generative AI

Autoencoders Types and Applications

Implement Autoencoders Using Python

Generative AI – Variational Autoencoders

ChatGPT A Generative AI Model

Generative AI in Manufacturing

Gen AI For Developers

Generative AI for Cybersecurity

Generative AI for Software Testing

Generative AI for Marketing

Generative AI for Educators

Generative AI for Healthcare

Generative AI for Students

Generative AI for Industry

Generative AI for Movies

Generative AI for Music

Generative AI for Cooking

Generative AI for Media

Generative AI for Communications

Generative AI for Photography

Curriculum

44 Sections
289 Lessons
10 Weeks

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Generative AI - Home
6
- 1.1
  overview
- 1.2
  What is Generative AI?
- 1.3
  How does Generative AI Differ From Other Types of AI?
- 1.4
  Applications of Generative AI
- 1.5
  Audience
- 1.6
  Prerequisites
FAQs on Generative AI
In this section, we have collected a set of Frequently Asked Questions on Generative AI followed by their answers −
17
- 2.1
  What is a Generative Adversarial Network (GAN)?
- 2.2
  What are some examples of GAN-generated content?
- 2.3
  What is a Variational Autoencoder (VAE)?
- 2.4
  What are some ethical considerations in Generative AI?
- 2.5
  What are the challenges of training Generative AI models?
- 2.6
  What are some popular tools and frameworks for working with Generative AI?
- 2.7
  Can Generative AI models be used for data augmentation?
- 2.8
  How can Generative AI be applied to text generation and natural language processing?
- 2.9
  How do I get started with building my own Generative AI models?
- 2.10
  What are conditional and unconditional Generative AI models?
- 2.11
  How do Generative AI models learn to mimic the style of input data?
- 2.12
  What are some techniques for controlling the diversity of generated outputs?
- 2.13
  How do you prevent Generative AI models from generating biased or offensive content?
- 2.14
  What are the computational requirements for training and deploying Generative AI models?
- 2.15
  What are the differences between generative models and traditional rule-based systems?
- 2.16
  What are the limitations of current Generative AI techniques?
- 2.17
  What are the main challenges in scaling Generative AI models to handle large datasets?
Basics of Generative AI
5
- 3.1
  overview
- 3.2
  Difference Between Traditional AI and Generative AI
- 3.3
  Use Cases of Generative AI
- 3.4
  Benefits of Generative AI
- 3.5
  Limitations of Generative AI
Evolution of Generative AI
5
- 4.1
  overview
- 4.2
  Early AI Exploration (1950s – 1980)
- 4.3
  Neural Network Resurgence (1980s – 2010)
- 4.4
  Deep Learning Dominance & Transformer Revolution (2010s – 2020)
- 4.5
  Specialized Generative Models (2020s – Present)
ML and Generative AI
5
- 5.1
  overview
- 5.2
  Machine Learning – A Brief Overview
- 5.3
  Contribution of ML in Generative AI
- 5.4
  Deep Learning – A Brief Overview
- 5.5
  Conclusion
Discriminative vs Generative Models
7
- 6.1
  overview
- 6.2
  What are Discriminative Models?
- 6.3
  Popular Discriminative Models
- 6.4
  What are Generative Models?
- 6.5
  Popular Generative Models
- 6.6
  Difference Between Discriminative and Generative Models
- 6.7
  Conclusion
Types of Generative Models
5
- 7.1
  overview
- 7.2
  Generative Adversarial Networks (GANs)
- 7.3
  Autoencoders
- 7.4
  Variational Autoencoders
- 7.5
  Conclusion
The Role of Probability Distribution in Generative Models
6
- 8.1
  overview
- 8.2
  What is Probability Distribution?
- 8.3
  Types of Probability Distributions
- 8.4
  Use of Probability Distributions in Generative Modeling
- 8.5
  Applications of Probability Distribution
- 8.6
  Conclusion
The Role of Probability Density Functions in Generative AI Models
5
- 9.1
  overview
- 9.2
  Understanding the Probability Density Function (PDF)
- 9.3
  Implementing Probability Density Function using Python
- 9.4
  Role of Probability Density Function in Generative Modeling
- 9.5
  Conclusion
Generative AI Models - Maximum Likelihood Estimation
2
- 10.1
  overview
- 10.2
  Understanding Maximum Likelihood Estimation (MLE)
MLE in Generative Modeling
4
- 11.1
  MLE in Generative Modeling
- 11.2
  Applications of MLE in Generative Modeling
- 11.3
  Implementing Maximum Likelihood Estimation using Python
- 11.4
  Conclusion
How do Generative Adversarial Networks (GAN) Work?
6
- 12.1
  overview
- 12.2
  What is a Generative Adversarial Network?
- 12.3
  The Generator Model
- 12.4
  The Discriminator Model
- 12.5
  How does a GAN Work?
- 12.6
  Conclusion
Generative Adversarial Network - Architecture and Types
5
- 13.1
  overview
- 13.2
  The Role of Generator in GAN Architecture
- 13.3
  The Role of Discriminator in GAN Architecture
- 13.4
  Types of Generative Adversarial Networks
- 13.5
  Conclusion
Conditional Generative Adversarial Networks (cGAN)
6
- 14.1
  What is a Conditional GAN?
- 14.2
  Where do We Need a Conditional GAN?
- 14.3
  Architecture of Conditional GANs
- 14.4
  Applications of Conditional GANs
- 14.5
  Challenges in using Conditional GANs
- 14.6
  Conclusion
CycleGAN and StyleGAN
9
- 15.1
  overview
- 15.2
  What is Cycle Generative Adversarial Network?
- 15.3
  How does a CycleGAN Work?
- 15.4
  Architecture of CycleGAN
- 15.5
  Applications of CycleGAN
- 15.6
  What is Style Generative Adversarial Network?
- 15.7
  Architecture of StyleGAN
- 15.8
  Applications of StyleGAN
- 15.9
  Conclusion
Training a Generative Adversarial Network (GANs)
4
- 16.1
  overview
- 16.2
  Process of Training a Generative Adversarial Network
- 16.3
  Training and Building a GAN
- 16.4
  Conclusion
Generative Adversarial Networks Applications
3
- 17.1
  overview
- 17.2
  Applications of Generative Adversarial Networks
- 17.3
  Conclusion
Transformers in Generative AI
6
- 18.1
  overview
- 18.2
  What is a Transformer Model?
- 18.3
  Key Components of the Transformer Model
- 18.4
  Why Do We Need Transformer Models?
- 18.5
  Difference between Transformers and Generative Adversarial Networks
- 18.6
  Conclusion
Architecture of Transformers in Generative AI
6
- 19.1
  overview
- 19.2
  How do Transformers Work in Generative AI?
- 19.3
  Transformer Architecture in Generative AI
- 19.4
  A Layer of the Encoder Stack of the Transformer
- 19.5
  A Layer of the Decoder Stack of the Transformer
- 19.6
  Conclusion
Input Embeddings in Transformers
6
- 20.1
  overview
- 20.2
  What are Input Embeddings?
- 20.3
  Importance of Input Embeddings in Transformers
- 20.4
  Working of the Input Embedding Sublayer
- 20.5
  How Input Embeddings are Implemented in a Transformer?
- 20.6
  Conclusion
Multi-Head Attention in Transformers
5
- 21.1
  overview
- 21.2
  What is Self-Attention Mechanism?
- 21.3
  What is Multi-Head Attention?
- 21.4
  Example
- 21.5
  Conclusion
Positional Encoding in Transformer Models
6
- 22.1
  overview
- 22.2
  What is Positional Encoding?
- 22.3
  Why Do We Need Positional Encoding in the Transformer Model?
- 22.4
  How Positional Encoding Works?
- 22.5
  Creating Positional Encodings Using Sinusoidal Functions
- 22.6
  Conclusion
Feed Forward Neural Network in Transformers
6
- 23.1
  overview
- 23.2
  Feed Forward Neural Network (FFNN) Sub-layer
- 23.3
  Mathematical Representation of FFNN Sublayer
- 23.4
  Importance of FFNN Sublayer in Transformer
- 23.5
  Python Implementation of Feed Forward Neural Network
- 23.6
  Conclusion
Normalization and Residual Connections
8
- 24.1
  overview
- 24.2
  Role of Layer Normalization and Residual Connections
- 24.3
  Residual Connections
- 24.4
  Layer Normalization
- 24.5
  Working of Normalization and Residual Connections
- 24.6
  Benefits of Normalization and Residual Connections
- 24.7
  Considerations for Normalization and Residual Connections
- 24.8
  Conclusion
Autoencoders in Generative AI
6
- 25.1
  overview
- 25.2
  What are Autoencoders?
- 25.3
  Architecture of Autoencoders
- 25.4
  How Do Autoencoders Work?
- 25.5
  Hyperparameter Tuning
- 25.6
  Conclusion
Autoencoders Types and Applications
7
- 26.1
  overview
- 26.2
  Vanilla Autoencoder
- 26.3
  Sparse Autoencoder
- 26.4
  Denoising Autoencoder
- 26.5
  Contractive Autoencoder
- 26.6
  Convolutional Autoencoder
- 26.7
  Conclusion
Implement Autoencoders Using Python
3
- 27.1
  overview
- 27.2
  Step-by-Step Guide to Implement Autoencoders Using Python
- 27.3
  Conclusion
Generative AI - Variational Autoencoders
6
- 28.1
  overview
- 28.2
  What are Variational Autoencoders?
- 28.3
  Traditional Autoencoders vs Variational Autoencoders
- 28.4
  Variational Autoencoder Loss Function
- 28.5
  Total VAE Loss
- 28.6
  Conclusion
ChatGPT A Generative AI Model
5
- 29.1
  overview
- 29.2
  What is ChatGPT?
- 29.3
  Relationship between Generative AI and ChatGPT
- 29.4
  Future of ChatGPT and Generative AI
- 29.5
  Conclusion
Generative AI in Manufacturing
8
- 30.1
  overview
- 30.2
  Design and Prototyping with Gen-AI Design Tools
- 30.3
  Process Optimization with AI-Powered Simulations
- 30.4
  Generative AI for Predictive Maintenance
- 30.5
  Supply Chain Optimization Using AI
- 30.6
  Quality Control with AI Vision Systems
- 30.7
  Generative AI for Document Automation
- 30.8
  Conclusion
Gen AI For Developers
8
- 31.1
  overview
- 31.2
  GitHub Copilot: AI-Powered Code Autocompletion
- 31.3
  ChatGPT: AI-Powered Coding Assistant
- 31.4
  DALL·E: AI-Powered Image Generation for UI/UX Design
- 31.5
  OpenAI Codex: AI for Code Generation and Documentation
- 31.6
  Tabnine: AI-Powered Code Completion
- 31.7
  Whisper: AI-Powered Speech-to-Text for Documentation
- 31.8
  Conclusion
Generative AI for Cybersecurity
8
- 32.1
  overview
- 32.2
  OpenAI GPT: AI for Security Automation and Threat Intelligence
- 32.3
  Microsoft Defender for Cloud: AI-Powered Threat Detection and Response
- 32.4
  Darktrace: AI-Driven Threat Detection and Response
- 32.5
  CrowdStrike Falcon: AI-Powered Endpoint Protection
- 32.6
  XSOAR: AI for Automated Incident Response
- 32.7
  Splunk AI: AI-Powered Security Analytics
- 32.8
  Conclusion
Generative AI for Software Testing
8
- 33.1
  overview
- 33.2
  ChatGPT: AI for Test Case Generation and Requirement Analysis
- 33.3
  Copilot (GitHub): AI for Code Completion and Automation Scripting
- 33.4
  Applitools: AI for Visual Testing
- 33.5
  Testim: AI for Automated Test Case Creation and Maintenance
- 33.6
  Snyk: AI for Security Testing and Vulnerability Detection
- 33.7
  Mabl: AI for End-to-End Testing and Performance Monitoring
- 33.8
  Conclusion
Generative AI for Marketing
8
- 34.1
  overview
- 34.2
  HubSpot’s Predictive Analytics: AI for Lead Scoring and Segmentation
- 34.3
  Jasper AI: Creative Copywriting and Brand Messaging
- 34.4
  HubSpot’s AI Content Assistant: Marketing Automation and Personalization
- 34.5
  Surfer SEO: AI for SEO-Optimized Content Creation
- 34.6
  Canva Magic Write: AI for Visual Content & Design
- 34.7
  Lumen5: AI for Video Content and Social Media Marketing
- 34.8
  Conclusion
Generative AI for Educators
9
- 35.1
  overview
- 35.2
  Table of Contents
- 35.3
  1. ChatGPT: AI-powered Tutoring and Content Creation
- 35.4
  2. DALL·E: Visual Content Creation
- 35.5
  3. Tome AI: AI-Powered Presentation Creation
- 35.6
  4. Murf AI: AI Voiceovers for Educational Content
- 35.7
  5. QuillBot: AI for Writing Assistance and Summarization
- 35.8
  6. Kahoot! – Interactive Learning and Quiz Tool
- 35.9
  Conclusion
Generative AI for Healthcare
8
- 36.1
  overview
- 36.2
  ChatGPT: AI for Patient Communication and Medical Information
- 36.3
  IBM Watson Health: AI for Clinical Decision Support
- 36.4
  Doximity’s Dialer AI: AI for Telehealth and Communication
- 36.5
  Butterfly iQ+: AI for Medical Imaging
- 36.6
  Suki AI: AI for Clinical Documentation and Note-Taking
- 36.7
  DeepMind’s AlphaFold: AI for Medical Research and Protein Structure Prediction
- 36.8
  Conclusion
Generative AI for Students
8
- 37.1
  overview
- 37.2
  ChatGPT: AI for Research, Essay Writing, and Study Assistance
- 37.3
  Grammarly: AI for Writing and Grammar Improvement
- 37.4
  QuillBot: AI for Paraphrasing and Summarizing Text
- 37.5
  Wolfram Alpha: AI for Solving Math and Science Problems
- 37.6
  Perplexity AI: AI for Quick Research and Fact Checking
- 37.7
  Notion AI: AI for Organizing Study Materials and Notes
- 37.8
  Conclusion
Generative AI for Industry
8
- 38.1
  overview
- 38.2
  ChatGPT: AI for Documentation, Reporting and Strategy
- 38.3
  Tableau + GPT-3: AI for Data Visualization and Insights
- 38.4
  IBM Watson Assistant: AI for Maintenance and Support
- 38.5
  DALL-E: AI for Visual Prototyping and Design Ideas
- 38.6
  AI-Driven Digital Twin: Simulation and Optimization
- 38.7
  UiPath: AI-Powered RPA for Workflow Automation
- 38.8
  Conclusion
Generative AI for Movies
8
- 39.1
  overview
- 39.2
  ChatGPT: AI for Screenwriting and Story Development
- 39.3
  Runway ML: AI for Visual Effects and Video Editing
- 39.4
  Descript: AI for Audio and Video Editing
- 39.5
  Storyboard That: AI for Storyboarding and Visual Planning
- 39.6
  DALL-E: AI for Concept Art and Visual Inspiration
- 39.7
  DeepBrain AI: Voice Synthesis and Character Voices
- 39.8
  Conclusion
Generative AI for Music
8
- 40.1
  overview
- 40.2
  ChatGPT: AI for Songwriting and Lyric Generation
- 40.3
  AIVA: AI for Composing Music
- 40.4
  Amper Music: AI for Royalty-Free Music Tracks
- 40.5
  LANDR: AI for Mastering and Production
- 40.6
  Endlesss: AI for Collaborative Music Production
- 40.7
  Sonic Visualiser: AI for Audio Analysis and Sound Design
- 40.8
  Conclusion
Generative AI for Cooking
8
- 41.1
  overview
- 41.2
  ChatGPT: AI for Recipe Development and Customization
- 41.3
  FoodAI: AI-Powered Recipe and Meal Planning
- 41.4
  Spoonacular: AI for Ingredient Pairing and Recipe Recommendations
- 41.5
  IBM Chef Watson: AI for Creative Recipe Development
- 41.6
  Whisk: AI for Recipe Collaboration and Scaling
- 41.7
  CulinaryAI: AI for Restaurant Menu Design and Optimization
- 41.8
  Conclusion
Generative AI for Media
7
- 42.1
  overview
- 42.2
  ChatGPT: AI for Content Creation and Ideation
- 42.3
  Canva: AI for Graphic Design and Visual Content
- 42.4
  Descript: AI for Audio and Video Editing
- 42.5
  Adobe Sensei: AI for Image and Video Enhancement
- 42.6
  Piktochart: AI for Infographic and Presentation Design
- 42.7
  Conclusion
Generative AI for Communications
8
- 43.1
  overview
- 43.2
  ChatGPT: AI for Content and Campaign Strategy
- 43.3
  Grammarly Business: AI for Messaging and Proofreading
- 43.4
  Tableau with GPT-3: AI for Data Storytelling and Analysis
- 43.5
  DALL-E: AI for Visual Content Creation and Branding
- 43.6
  Synthesia: AI for Video Content and Message Personalization
- 43.7
  Jasper AI: AI for Marketing Content and SEO-Friendly Copy
- 43.8
  Conclusion
Generative AI for Photography
7
- 44.1
  overview
- 44.2
  Adobe Firefly: AI for Image Editing and Enhancement
- 44.3
  DALL-E: AI for Concept Visualization and Creative
- 44.4
  Topaz Labs: AI for Image Restoration and Enhancement
- 44.5
  Midjourney: AI for Visual Storytelling and Creative Inspiration
- 44.6
  Photoshop Generative Fill: AI for Image Manipulation and Editing
- 44.7
  Conclusion

Curriculum

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Generative AI Tutorial

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