Generative AI: Architecting the Future of Business
Unlock the transformative potential of Generative AI and revolutionize your business strategy. This comprehensive course provides you with the knowledge, skills, and practical experience to leverage Generative AI technologies for innovation, efficiency, and competitive advantage. Learn from expert instructors through interactive sessions, hands-on projects, and real-world case studies. Earn your certificate upon completion, validating your expertise in this cutting-edge field. This course is designed to be Interactive, Engaging, Comprehensive, Personalized, Up-to-date, Practical, Real-world applications, High-quality content, Expert instructors, Certification, Flexible learning, User-friendly, Mobile-accessible, Community-driven, Actionable insights, Hands-on projects, Bite-sized lessons, Lifetime access, Gamification, Progress tracking.Upon successful completion of this course, you will receive a certificate issued by The Art of Service, recognizing your expertise in Generative AI.
Course Curriculum
Module 1: Introduction to Generative AI – Foundations and Landscape - Topic 1.1: What is Generative AI? Defining the Core Concepts and Scope
- Topic 1.2: The History and Evolution of Generative AI – From Markov Chains to Modern Transformers
- Topic 1.3: Key Generative AI Models: GANs, VAEs, Transformers, Diffusion Models – An Overview
- Topic 1.4: Generative AI vs. Traditional AI: Understanding the Differences and Complementarities
- Topic 1.5: Ethical Considerations and Responsible AI Development – Bias, Fairness, and Transparency
- Topic 1.6: The Generative AI Ecosystem: Players, Technologies, and Trends
- Topic 1.7: Setting up Your Development Environment: Cloud Platforms and Tools (Google Colab, Jupyter Notebooks)
- Topic 1.8: Foundational Python Libraries for Generative AI: TensorFlow, PyTorch, Keras
Module 2: Generative Adversarial Networks (GANs) – Deep Dive and Applications - Topic 2.1: Understanding the GAN Architecture: Generator and Discriminator
- Topic 2.2: Training GANs: Loss Functions, Optimization Techniques, and Challenges
- Topic 2.3: Implementing a Basic GAN for Image Generation using TensorFlow/PyTorch
- Topic 2.4: Conditional GANs (cGANs): Generating Images based on Labels
- Topic 2.5: Deep Convolutional GANs (DCGANs): Leveraging CNNs for High-Resolution Image Generation
- Topic 2.6: StyleGAN: Controlling Style and Visual Attributes in Generated Images
- Topic 2.7: GANs for Image-to-Image Translation: Pix2Pix and CycleGAN
- Topic 2.8: GAN Applications: Image Enhancement, Super-Resolution, and Anomaly Detection
- Topic 2.9: Evaluating GAN Performance: Metrics and Techniques
- Topic 2.10: Addressing Common GAN Challenges: Mode Collapse and Training Instability
Module 3: Variational Autoencoders (VAEs) – Probabilistic Generative Models - Topic 3.1: Understanding the VAE Architecture: Encoder, Decoder, and Latent Space
- Topic 3.2: The Role of Variational Inference in VAEs
- Topic 3.3: Implementing a Basic VAE for Image Generation using TensorFlow/PyTorch
- Topic 3.4: Conditional VAEs: Generating Data with Specific Attributes
- Topic 3.5: Beta-VAEs: Controlling the Disentanglement of Latent Space
- Topic 3.6: VAE Applications: Data Generation, Representation Learning, and Anomaly Detection
- Topic 3.7: Evaluating VAE Performance: Metrics and Techniques
- Topic 3.8: Comparing VAEs and GANs: Strengths and Weaknesses
Module 4: Transformers and Attention Mechanisms – The Foundation of Modern Generative AI - Topic 4.1: Introduction to Attention Mechanisms: Query, Key, and Value
- Topic 4.2: Self-Attention: Capturing Relationships within a Sequence
- Topic 4.3: Multi-Head Attention: Enhancing Attention with Multiple Representations
- Topic 4.4: The Transformer Architecture: Encoder and Decoder Stacks
- Topic 4.5: Positional Encoding: Incorporating Sequence Order into Transformers
- Topic 4.6: Implementing a Transformer Model from Scratch
- Topic 4.7: Transformers for Text Generation: GPT Models (GPT-2, GPT-3)
- Topic 4.8: Transformers for Image Generation: Vision Transformers (ViT) and DALL-E
- Topic 4.9: Fine-tuning Pre-trained Transformer Models for Specific Tasks
Module 5: Large Language Models (LLMs) – Mastering Text Generation and Understanding - Topic 5.1: Deep Dive into GPT-3 and Other Large Language Models
- Topic 5.2: Prompt Engineering: Designing Effective Prompts for LLMs
- Topic 5.3: Zero-Shot, One-Shot, and Few-Shot Learning with LLMs
- Topic 5.4: Using LLMs for Text Summarization, Translation, and Question Answering
- Topic 5.5: Fine-tuning LLMs for Specific Domains and Tasks
- Topic 5.6: Evaluating LLM Performance: Metrics and Benchmarks
- Topic 5.7: Ethical Considerations for LLMs: Bias, Misinformation, and Safety
- Topic 5.8: Techniques for Mitigating Bias in LLMs
- Topic 5.9: Deploying LLMs in Real-World Applications
- Topic 5.10: The Future of LLMs: Emerging Trends and Research Directions
Module 6: Diffusion Models – High-Quality Image Generation and Beyond - Topic 6.1: Understanding Diffusion Models: Forward and Reverse Processes
- Topic 6.2: Denoising Diffusion Probabilistic Models (DDPMs)
- Topic 6.3: Implementing a Basic Diffusion Model for Image Generation
- Topic 6.4: Conditional Diffusion Models: Generating Images based on Text Prompts
- Topic 6.5: Guided Diffusion: Enhancing Image Quality and Control
- Topic 6.6: Applications of Diffusion Models: Image Editing, Inpainting, and Super-Resolution
- Topic 6.7: Comparing Diffusion Models to GANs and VAEs
- Topic 6.8: The Advantages of Diffusion Models: Stability and High-Fidelity Output
Module 7: Generative AI for Content Creation – Text, Images, Audio, and Video - Topic 7.1: Generating Realistic Images with Generative AI for Marketing and Advertising
- Topic 7.2: Creating AI-Powered Music and Audio Content
- Topic 7.3: Developing AI-Generated Videos for Explainer Videos and Product Demos
- Topic 7.4: Automating Content Creation for Social Media and Blogs
- Topic 7.5: Using Generative AI for Personalized Content Recommendations
- Topic 7.6: Tools and Platforms for Generative AI Content Creation
- Topic 7.7: Legal and Ethical Considerations for AI-Generated Content
Module 8: Generative AI for Product Development and Design – Innovation and Efficiency - Topic 8.1: Using Generative AI for Product Ideation and Concept Generation
- Topic 8.2: Automating the Design Process with Generative Design Tools
- Topic 8.3: Optimizing Product Performance with AI-Driven Simulations
- Topic 8.4: Creating Personalized Product Designs with Generative AI
- Topic 8.5: Applying Generative AI in Manufacturing and Supply Chain Management
- Topic 8.6: Case Studies: Generative AI in Automotive, Aerospace, and Consumer Goods
Module 9: Generative AI for Marketing and Sales – Personalization and Engagement - Topic 9.1: Personalizing Customer Experiences with AI-Generated Content
- Topic 9.2: Automating Marketing Campaigns with Generative AI
- Topic 9.3: Using AI-Generated Chatbots for Customer Support and Sales
- Topic 9.4: Creating AI-Powered Personalized Product Recommendations
- Topic 9.5: Generating Leads with AI-Driven Content Marketing
- Topic 9.6: Analyzing Customer Sentiment with Generative AI
Module 10: Generative AI for Finance – Risk Management and Fraud Detection - Topic 10.1: Using Generative AI for Financial Modeling and Forecasting
- Topic 10.2: Detecting Fraud with AI-Powered Anomaly Detection
- Topic 10.3: Automating Risk Management with Generative AI
- Topic 10.4: Using AI-Generated Synthetic Data for Financial Analysis
- Topic 10.5: Improving Customer Service with AI-Driven Chatbots
- Topic 10.6: Compliance and Regulatory Considerations for Generative AI in Finance
Module 11: Generative AI for Healthcare – Diagnostics and Drug Discovery - Topic 11.1: Accelerating Drug Discovery with Generative AI
- Topic 11.2: Improving Medical Diagnostics with AI-Powered Image Analysis
- Topic 11.3: Creating Personalized Treatment Plans with Generative AI
- Topic 11.4: Using AI-Generated Synthetic Data for Medical Research
- Topic 11.5: Automating Medical Record Analysis with Generative AI
- Topic 11.6: Ethical Considerations and Patient Privacy in Healthcare AI
Module 12: Building a Generative AI Strategy for Your Business – From Vision to Implementation - Topic 12.1: Identifying Business Opportunities for Generative AI
- Topic 12.2: Defining Your Generative AI Vision and Goals
- Topic 12.3: Assessing Your Data Infrastructure and Readiness
- Topic 12.4: Choosing the Right Generative AI Models and Tools
- Topic 12.5: Building a Generative AI Team and Skillset
- Topic 12.6: Implementing a Pilot Project and Measuring Success
- Topic 12.7: Scaling Your Generative AI Initiatives Across the Organization
- Topic 12.8: Managing the Risks and Ethical Considerations of Generative AI
Module 13: Deploying and Scaling Generative AI Applications - Topic 13.1: Choosing the Right Infrastructure for Generative AI Deployment (Cloud, On-Premise, Hybrid)
- Topic 13.2: Optimizing Generative AI Models for Performance and Scalability
- Topic 13.3: Using Containerization (Docker) and Orchestration (Kubernetes) for Deployment
- Topic 13.4: Monitoring and Managing Generative AI Applications in Production
- Topic 13.5: Implementing CI/CD Pipelines for Generative AI Model Updates
- Topic 13.6: Scaling Generative AI Infrastructure to Meet Growing Demand
Module 14: Advanced Topics in Generative AI - Topic 14.1: Generative AI for 3D Modeling and Design
- Topic 14.2: Generative AI for Robotics and Automation
- Topic 14.3: Generative AI for Synthetic Data Generation
- Topic 14.4: Generative AI for Reinforcement Learning
- Topic 14.5: Meta-Learning for Generative AI
Module 15: The Future of Generative AI – Emerging Trends and Opportunities - Topic 15.1: The Convergence of Generative AI and Other AI Technologies
- Topic 15.2: The Role of Generative AI in the Metaverse
- Topic 15.3: The Impact of Generative AI on the Future of Work
- Topic 15.4: The Ethical and Societal Implications of Generative AI
- Topic 15.5: Staying Ahead of the Curve: Continuous Learning and Skill Development
Module 16: Hands-on Project 1: Building a GAN for Fashion Image Generation - Topic 16.1: Data Collection and Preprocessing
- Topic 16.2: Implementing a DCGAN Model
- Topic 16.3: Training and Evaluating the Model
- Topic 16.4: Generating New Fashion Images
Module 17: Hands-on Project 2: Fine-tuning a GPT Model for Text Summarization - Topic 17.1: Data Collection and Preprocessing
- Topic 17.2: Fine-tuning a Pre-trained GPT Model
- Topic 17.3: Evaluating the Model's Performance
- Topic 17.4: Generating Summaries of Long Texts
Module 18: Hands-on Project 3: Building a Diffusion Model for Image Inpainting - Topic 18.1: Data Collection and Preprocessing
- Topic 18.2: Implementing a Diffusion Model
- Topic 18.3: Training and Evaluating the Model
- Topic 18.4: Inpainting Damaged Images
Module 19: Case Study 1: Generative AI in the Automotive Industry - Topic 19.1: Generative Design for Car Body Optimization
- Topic 19.2: AI-Powered Interior Design
- Topic 19.3: Synthetic Data for Autonomous Vehicle Training
Module 20: Case Study 2: Generative AI in the Fashion Industry - Topic 20.1: AI-Generated Fashion Designs
- Topic 20.2: Personalized Clothing Recommendations
- Topic 20.3: Virtual Try-On Experiences
Module 21: Case Study 3: Generative AI in the Entertainment Industry - Topic 21.1: AI-Generated Music and Sound Effects
- Topic 21.2: Creating Realistic Visual Effects
- Topic 21.3: Developing AI-Powered Games
Module 22: Understanding the Ethical Implications of Generative AI - Topic 22.1: Bias and Fairness in Generative AI Models
- Topic 22.2: The Impact of Generative AI on the Job Market
- Topic 22.3: Misinformation and Deepfakes
Module 23: Strategies for Responsible AI Development - Topic 23.1: Data Transparency and Accountability
- Topic 23.2: Model Explainability and Interpretability
- Topic 23.3: Implementing Ethical Guidelines for AI Development
Module 24: The Legal Landscape of Generative AI - Topic 24.1: Copyright and Intellectual Property Issues
- Topic 24.2: Data Privacy and Security
- Topic 24.3: Regulatory Compliance for AI Applications
Module 25: Model Evaluation and Validation Techniques - Topic 25.1: Quantitative Metrics for Assessing Model Performance
- Topic 25.2: Qualitative Evaluation Methods
- Topic 25.3: Addressing Overfitting and Underfitting
Module 26: Hyperparameter Tuning and Optimization - Topic 26.1: Grid Search and Random Search
- Topic 26.2: Bayesian Optimization
- Topic 26.3: Automated Machine Learning (AutoML) Techniques
Module 27: Transfer Learning and Pre-trained Models - Topic 27.1: Leveraging Pre-trained Models for Faster Training
- Topic 27.2: Fine-tuning Models for Specific Tasks
- Topic 27.3: Domain Adaptation Techniques
Module 28: Data Augmentation Strategies - Topic 28.1: Image Data Augmentation Techniques
- Topic 28.2: Text Data Augmentation Methods
- Topic 28.3: Augmenting Other Data Types
Module 29: Generative AI for Drug Design and Discovery - Topic 29.1: De Novo Molecular Design
- Topic 29.2: Target Identification and Validation
- Topic 29.3: Predicting Drug Interactions and Toxicity
Module 30: Generative AI for Materials Science - Topic 30.1: Designing New Materials with Desired Properties
- Topic 30.2: Optimizing Material Processing Parameters
- Topic 30.3: Predicting Material Performance and Durability
Module 31: Generative AI for Financial Trading - Topic 31.1: Algorithmic Trading Strategies
- Topic 31.2: Risk Management and Portfolio Optimization
- Topic 31.3: Fraud Detection and Prevention
Module 32: Generative AI for Climate Modeling - Topic 32.1: Generating Realistic Climate Scenarios
- Topic 32.2: Predicting the Impact of Climate Change
- Topic 32.3: Developing Sustainable Solutions
Module 33: Generative AI for Art and Music Creation - Topic 33.1: Generating Original Artwork
- Topic 33.2: Composing New Music
- Topic 33.3: Creating Interactive Art Installations
Module 34: Building a Generative AI Community - Topic 34.1: Engaging with Other Practitioners
- Topic 34.2: Sharing Knowledge and Resources
- Topic 34.3: Collaborating on Projects
Module 35: Contributing to Open Source Generative AI Projects - Topic 35.1: Finding Opportunities to Contribute
- Topic 35.2: Following Contribution Guidelines
- Topic 35.3: Giving Back to the Community
Module 36: Staying Up-to-Date with the Latest Generative AI Research - Topic 36.1: Following Key Researchers and Institutions
- Topic 36.2: Reading Research Papers
- Topic 36.3: Attending Conferences and Workshops
Module 37: Introduction to GAN Inversion - Topic 37.1: Understanding GAN Inversion Techniques
- Topic 37.2: Applications of GAN Inversion
Module 38: Exploring Style Transfer Techniques - Topic 38.1: Neural Style Transfer Methods
- Topic 38.2: Applying Style Transfer to Images and Videos
Module 39: Generative Models for Video Synthesis - Topic 39.1: Creating Realistic Video Content
- Topic 39.2: Video Editing and Manipulation
Module 40: Text-to-Image Generation with DALL-E and Stable Diffusion - Topic 40.1: Using Text Prompts to Generate Images
- Topic 40.2: Exploring Different Text-to-Image Models
Module 41: Image Super-Resolution with Generative Models - Topic 41.1: Enhancing Image Quality with AI
- Topic 41.2: Super-Resolving Low-Resolution Images
Module 42: Introduction to Generative Agents - Topic 42.1: Creating Intelligent Virtual Characters
- Topic 42.2: Developing Interactive AI Agents
Module 43: Building a Generative AI-Powered Chatbot - Topic 43.1: Training a Chatbot with Generative Models
- Topic 43.2: Deploying a Chatbot for Customer Service
Module 44: Generative AI for Code Generation - Topic 44.1: Automating Code Development
- Topic 44.2: Generating Code from Natural Language Descriptions
Module 45: Understanding Generative AI for Data Augmentation in Healthcare - Topic 45.1: Synthetic medical image generation
- Topic 45.2: Enhanced training datasets
Module 46: Exploring Generative AI for Anomaly Detection in Manufacturing - Topic 46.1: Identifying defects and irregularities
- Topic 46.2: Improving quality control processes
Module 47: Investigating Generative AI for Personalized Education - Topic 47.1: Customized learning materials
- Topic 47.2: Adaptive tutoring systems
Module 48: Analyzing Generative AI for Supply Chain Optimization - Topic 48.1: Predicting demand and optimizing logistics
- Topic 48.2: Improving efficiency and reducing costs
Module 49: Applying Generative AI for Drug Repurposing - Topic 49.1: Identifying new uses for existing drugs
- Topic 49.2: Accelerating the drug development process
Module 50: The Role of Generative AI in Personalized Medicine - Topic 50.1: Tailoring treatments to individual patients
- Topic 50.2: Improving patient outcomes
Module 51: Generative AI for Predictive Maintenance - Topic 51.1: Predicting Equipment Failure
- Topic 51.2: Optimizing Maintenance Schedules
Module 52: Generative AI for Cybersecurity Threat Detection - Topic 52.1: Identifying Malicious Activities
- Topic 52.2: Enhancing Security Measures
Module 53: Generating Realistic Training Data for Autonomous Vehicles - Topic 53.1: Simulated Driving Environments
- Topic 53.2: Enhancing the Safety and Reliability of Autonomous Systems
Module 54: Generative AI for Personalized Financial Planning - Topic 54.1: Customized Investment Strategies
- Topic 54.2: Improving Financial Outcomes
Module 55: Using Generative AI for Enhanced Customer Service Experiences - Topic 55.1: Personalized interactions
- Topic 55.2: Improving Customer Satisfaction
Module 56: Generative AI for Creating Immersive Gaming Experiences - Topic 56.1: Dynamically Generated Game Content
- Topic 56.2: Engaging and Realistic Game Worlds
Module 57: The Potential of Generative AI for Content Localization - Topic 57.1: Automatic Translation and Adaptation
- Topic 57.2: Expanding Global Reach
Module 58: Generative AI for Creating Interactive Storytelling Experiences - Topic 58.1: Branching Narratives
- Topic 58.2: Personalized Story Outcomes
Module 59: Generative AI for Virtual Reality Content Creation - Topic 59.1: Generating Immersive Environments
- Topic 59.2: Creating Engaging VR Experiences
Module 60: Building a Scalable Generative AI Platform - Topic 60.1: Infrastructure and Architecture
- Topic 60.2: Performance Optimization
Module 61: MLOps for Generative AI - Topic 61.1: Managing the Machine Learning Lifecycle
- Topic 61.2: Deployment, Monitoring and Scaling of GenAI models
Module 62: Securing GenAI Models - Topic 62.1: Data Poisoning, Prompt Hacking and other risks
- Topic 62.2: Security best practices for Generative AI
Module 63: Data Governance for GenAI - Topic 63.1: Managing training data
- Topic 63.2: Data usage policies for Generated content
Module 64: GenAI for Code Completion - Topic 64.1: Generate useful Code Snippets for various IDEs
- Topic 64.2: Code testing and Reviewing
Module 65: GenAI for Automating documentation - Topic 65.1: Documentation automation
- Topic 65.2: Generate API documentation
Module 66: GenAI for Content Moderation - Topic 66.1: Automating Content Moderation with GenAI
- Topic 66.2: Creating AI-Powered Content Moderation policies
Module 67: GenAI for Summarization - Topic 67.1: Automating Summarization with GenAI
- Topic 67.2: Meeting Minutes Summarization
Module 68: GenAI for Customer Segmentation - Topic 68.1: Automating Customer Segmentation with GenAI
- Topic 68.2: Customer Behaviour Analysis
Module 69: GenAI for Dynamic Content - Topic 69.1: Automating Dynamic Content Generation with GenAI
- Topic 69.2: Personalized product offerings
Module 70: GenAI for Brand Monitoring - Topic 70.1: Automating Brand Monitoring with GenAI
- Topic 70.2: Improving brand reputation
Module 71: GenAI for Fraud Detection - Topic 71.1: Automating Fraud Detection with GenAI
- Topic 71.2: Improve fraud protection
Module 72: GenAI for Legal Discovery - Topic 72.1: Automating Legal Discovery with GenAI
- Topic 72.2: Reducing legal risks
Module 73: GenAI for Market Research - Topic 73.1: Automating Market Research with GenAI
- Topic 73.2: Improving business strategy
Module 74: GenAI for Training - Topic 74.1: Automating Training Content Generation with GenAI
- Topic 74.2: Improving user competency
Module 75: GenAI for Translation - Topic 75.1: Automating Translation with GenAI
- Topic 75.2: Multilingual support
Module 76: GenAI for Voice Cloning - Topic 76.1: Automating Voice Cloning with GenAI
- Topic 76.2: Generate voice based product
Module 77: GenAI for Chatbots - Topic 77.1: Automating Chatbot conversation flow
- Topic 77.2: Improved customer engagement
Module 78: GenAI for Virtual Assistant - Topic 78.1: Automating Virtual Assistant workflows
- Topic 78.2: Improve operational efficiency
Module 79: GenAI for Story Telling - Topic 79.1: Automating Story Telling workflow
- Topic 79.2: Interactive storytelling
Module 80: GenAI for Game Design - Topic 80.1: Automating Game Design workflow
- Topic 80.2: Generate new game ideas
Module 81: Advanced Prompt Engineering Techniques - Topic 81.1: Chain of thought prompting
- Topic 81.2: Tree of Thoughts
Module 82: Generative AI for Synthetic Data Generation - Topic 82.1: Generate tabular Synthetic data
- Topic 82.2: Generate image and video Synthetic data
Module 1: Introduction to Generative AI – Foundations and Landscape - Topic 1.1: What is Generative AI? Defining the Core Concepts and Scope
- Topic 1.2: The History and Evolution of Generative AI – From Markov Chains to Modern Transformers
- Topic 1.3: Key Generative AI Models: GANs, VAEs, Transformers, Diffusion Models – An Overview
- Topic 1.4: Generative AI vs. Traditional AI: Understanding the Differences and Complementarities
- Topic 1.5: Ethical Considerations and Responsible AI Development – Bias, Fairness, and Transparency
- Topic 1.6: The Generative AI Ecosystem: Players, Technologies, and Trends
- Topic 1.7: Setting up Your Development Environment: Cloud Platforms and Tools (Google Colab, Jupyter Notebooks)
- Topic 1.8: Foundational Python Libraries for Generative AI: TensorFlow, PyTorch, Keras
Module 2: Generative Adversarial Networks (GANs) – Deep Dive and Applications - Topic 2.1: Understanding the GAN Architecture: Generator and Discriminator
- Topic 2.2: Training GANs: Loss Functions, Optimization Techniques, and Challenges
- Topic 2.3: Implementing a Basic GAN for Image Generation using TensorFlow/PyTorch
- Topic 2.4: Conditional GANs (cGANs): Generating Images based on Labels
- Topic 2.5: Deep Convolutional GANs (DCGANs): Leveraging CNNs for High-Resolution Image Generation
- Topic 2.6: StyleGAN: Controlling Style and Visual Attributes in Generated Images
- Topic 2.7: GANs for Image-to-Image Translation: Pix2Pix and CycleGAN
- Topic 2.8: GAN Applications: Image Enhancement, Super-Resolution, and Anomaly Detection
- Topic 2.9: Evaluating GAN Performance: Metrics and Techniques
- Topic 2.10: Addressing Common GAN Challenges: Mode Collapse and Training Instability
Module 3: Variational Autoencoders (VAEs) – Probabilistic Generative Models - Topic 3.1: Understanding the VAE Architecture: Encoder, Decoder, and Latent Space
- Topic 3.2: The Role of Variational Inference in VAEs
- Topic 3.3: Implementing a Basic VAE for Image Generation using TensorFlow/PyTorch
- Topic 3.4: Conditional VAEs: Generating Data with Specific Attributes
- Topic 3.5: Beta-VAEs: Controlling the Disentanglement of Latent Space
- Topic 3.6: VAE Applications: Data Generation, Representation Learning, and Anomaly Detection
- Topic 3.7: Evaluating VAE Performance: Metrics and Techniques
- Topic 3.8: Comparing VAEs and GANs: Strengths and Weaknesses
Module 4: Transformers and Attention Mechanisms – The Foundation of Modern Generative AI - Topic 4.1: Introduction to Attention Mechanisms: Query, Key, and Value
- Topic 4.2: Self-Attention: Capturing Relationships within a Sequence
- Topic 4.3: Multi-Head Attention: Enhancing Attention with Multiple Representations
- Topic 4.4: The Transformer Architecture: Encoder and Decoder Stacks
- Topic 4.5: Positional Encoding: Incorporating Sequence Order into Transformers
- Topic 4.6: Implementing a Transformer Model from Scratch
- Topic 4.7: Transformers for Text Generation: GPT Models (GPT-2, GPT-3)
- Topic 4.8: Transformers for Image Generation: Vision Transformers (ViT) and DALL-E
- Topic 4.9: Fine-tuning Pre-trained Transformer Models for Specific Tasks
Module 5: Large Language Models (LLMs) – Mastering Text Generation and Understanding - Topic 5.1: Deep Dive into GPT-3 and Other Large Language Models
- Topic 5.2: Prompt Engineering: Designing Effective Prompts for LLMs
- Topic 5.3: Zero-Shot, One-Shot, and Few-Shot Learning with LLMs
- Topic 5.4: Using LLMs for Text Summarization, Translation, and Question Answering
- Topic 5.5: Fine-tuning LLMs for Specific Domains and Tasks
- Topic 5.6: Evaluating LLM Performance: Metrics and Benchmarks
- Topic 5.7: Ethical Considerations for LLMs: Bias, Misinformation, and Safety
- Topic 5.8: Techniques for Mitigating Bias in LLMs
- Topic 5.9: Deploying LLMs in Real-World Applications
- Topic 5.10: The Future of LLMs: Emerging Trends and Research Directions
Module 6: Diffusion Models – High-Quality Image Generation and Beyond - Topic 6.1: Understanding Diffusion Models: Forward and Reverse Processes
- Topic 6.2: Denoising Diffusion Probabilistic Models (DDPMs)
- Topic 6.3: Implementing a Basic Diffusion Model for Image Generation
- Topic 6.4: Conditional Diffusion Models: Generating Images based on Text Prompts
- Topic 6.5: Guided Diffusion: Enhancing Image Quality and Control
- Topic 6.6: Applications of Diffusion Models: Image Editing, Inpainting, and Super-Resolution
- Topic 6.7: Comparing Diffusion Models to GANs and VAEs
- Topic 6.8: The Advantages of Diffusion Models: Stability and High-Fidelity Output
Module 7: Generative AI for Content Creation – Text, Images, Audio, and Video - Topic 7.1: Generating Realistic Images with Generative AI for Marketing and Advertising
- Topic 7.2: Creating AI-Powered Music and Audio Content
- Topic 7.3: Developing AI-Generated Videos for Explainer Videos and Product Demos
- Topic 7.4: Automating Content Creation for Social Media and Blogs
- Topic 7.5: Using Generative AI for Personalized Content Recommendations
- Topic 7.6: Tools and Platforms for Generative AI Content Creation
- Topic 7.7: Legal and Ethical Considerations for AI-Generated Content
Module 8: Generative AI for Product Development and Design – Innovation and Efficiency - Topic 8.1: Using Generative AI for Product Ideation and Concept Generation
- Topic 8.2: Automating the Design Process with Generative Design Tools
- Topic 8.3: Optimizing Product Performance with AI-Driven Simulations
- Topic 8.4: Creating Personalized Product Designs with Generative AI
- Topic 8.5: Applying Generative AI in Manufacturing and Supply Chain Management
- Topic 8.6: Case Studies: Generative AI in Automotive, Aerospace, and Consumer Goods
Module 9: Generative AI for Marketing and Sales – Personalization and Engagement - Topic 9.1: Personalizing Customer Experiences with AI-Generated Content
- Topic 9.2: Automating Marketing Campaigns with Generative AI
- Topic 9.3: Using AI-Generated Chatbots for Customer Support and Sales
- Topic 9.4: Creating AI-Powered Personalized Product Recommendations
- Topic 9.5: Generating Leads with AI-Driven Content Marketing
- Topic 9.6: Analyzing Customer Sentiment with Generative AI
Module 10: Generative AI for Finance – Risk Management and Fraud Detection - Topic 10.1: Using Generative AI for Financial Modeling and Forecasting
- Topic 10.2: Detecting Fraud with AI-Powered Anomaly Detection
- Topic 10.3: Automating Risk Management with Generative AI
- Topic 10.4: Using AI-Generated Synthetic Data for Financial Analysis
- Topic 10.5: Improving Customer Service with AI-Driven Chatbots
- Topic 10.6: Compliance and Regulatory Considerations for Generative AI in Finance
Module 11: Generative AI for Healthcare – Diagnostics and Drug Discovery - Topic 11.1: Accelerating Drug Discovery with Generative AI
- Topic 11.2: Improving Medical Diagnostics with AI-Powered Image Analysis
- Topic 11.3: Creating Personalized Treatment Plans with Generative AI
- Topic 11.4: Using AI-Generated Synthetic Data for Medical Research
- Topic 11.5: Automating Medical Record Analysis with Generative AI
- Topic 11.6: Ethical Considerations and Patient Privacy in Healthcare AI
Module 12: Building a Generative AI Strategy for Your Business – From Vision to Implementation - Topic 12.1: Identifying Business Opportunities for Generative AI
- Topic 12.2: Defining Your Generative AI Vision and Goals
- Topic 12.3: Assessing Your Data Infrastructure and Readiness
- Topic 12.4: Choosing the Right Generative AI Models and Tools
- Topic 12.5: Building a Generative AI Team and Skillset
- Topic 12.6: Implementing a Pilot Project and Measuring Success
- Topic 12.7: Scaling Your Generative AI Initiatives Across the Organization
- Topic 12.8: Managing the Risks and Ethical Considerations of Generative AI
Module 13: Deploying and Scaling Generative AI Applications - Topic 13.1: Choosing the Right Infrastructure for Generative AI Deployment (Cloud, On-Premise, Hybrid)
- Topic 13.2: Optimizing Generative AI Models for Performance and Scalability
- Topic 13.3: Using Containerization (Docker) and Orchestration (Kubernetes) for Deployment
- Topic 13.4: Monitoring and Managing Generative AI Applications in Production
- Topic 13.5: Implementing CI/CD Pipelines for Generative AI Model Updates
- Topic 13.6: Scaling Generative AI Infrastructure to Meet Growing Demand
Module 14: Advanced Topics in Generative AI - Topic 14.1: Generative AI for 3D Modeling and Design
- Topic 14.2: Generative AI for Robotics and Automation
- Topic 14.3: Generative AI for Synthetic Data Generation
- Topic 14.4: Generative AI for Reinforcement Learning
- Topic 14.5: Meta-Learning for Generative AI
Module 15: The Future of Generative AI – Emerging Trends and Opportunities - Topic 15.1: The Convergence of Generative AI and Other AI Technologies
- Topic 15.2: The Role of Generative AI in the Metaverse
- Topic 15.3: The Impact of Generative AI on the Future of Work
- Topic 15.4: The Ethical and Societal Implications of Generative AI
- Topic 15.5: Staying Ahead of the Curve: Continuous Learning and Skill Development
Module 16: Hands-on Project 1: Building a GAN for Fashion Image Generation - Topic 16.1: Data Collection and Preprocessing
- Topic 16.2: Implementing a DCGAN Model
- Topic 16.3: Training and Evaluating the Model
- Topic 16.4: Generating New Fashion Images
Module 17: Hands-on Project 2: Fine-tuning a GPT Model for Text Summarization - Topic 17.1: Data Collection and Preprocessing
- Topic 17.2: Fine-tuning a Pre-trained GPT Model
- Topic 17.3: Evaluating the Model's Performance
- Topic 17.4: Generating Summaries of Long Texts
Module 18: Hands-on Project 3: Building a Diffusion Model for Image Inpainting - Topic 18.1: Data Collection and Preprocessing
- Topic 18.2: Implementing a Diffusion Model
- Topic 18.3: Training and Evaluating the Model
- Topic 18.4: Inpainting Damaged Images
Module 19: Case Study 1: Generative AI in the Automotive Industry - Topic 19.1: Generative Design for Car Body Optimization
- Topic 19.2: AI-Powered Interior Design
- Topic 19.3: Synthetic Data for Autonomous Vehicle Training
Module 20: Case Study 2: Generative AI in the Fashion Industry - Topic 20.1: AI-Generated Fashion Designs
- Topic 20.2: Personalized Clothing Recommendations
- Topic 20.3: Virtual Try-On Experiences
Module 21: Case Study 3: Generative AI in the Entertainment Industry - Topic 21.1: AI-Generated Music and Sound Effects
- Topic 21.2: Creating Realistic Visual Effects
- Topic 21.3: Developing AI-Powered Games
Module 22: Understanding the Ethical Implications of Generative AI - Topic 22.1: Bias and Fairness in Generative AI Models
- Topic 22.2: The Impact of Generative AI on the Job Market
- Topic 22.3: Misinformation and Deepfakes
Module 23: Strategies for Responsible AI Development - Topic 23.1: Data Transparency and Accountability
- Topic 23.2: Model Explainability and Interpretability
- Topic 23.3: Implementing Ethical Guidelines for AI Development
Module 24: The Legal Landscape of Generative AI - Topic 24.1: Copyright and Intellectual Property Issues
- Topic 24.2: Data Privacy and Security
- Topic 24.3: Regulatory Compliance for AI Applications
Module 25: Model Evaluation and Validation Techniques - Topic 25.1: Quantitative Metrics for Assessing Model Performance
- Topic 25.2: Qualitative Evaluation Methods
- Topic 25.3: Addressing Overfitting and Underfitting
Module 26: Hyperparameter Tuning and Optimization - Topic 26.1: Grid Search and Random Search
- Topic 26.2: Bayesian Optimization
- Topic 26.3: Automated Machine Learning (AutoML) Techniques
Module 27: Transfer Learning and Pre-trained Models - Topic 27.1: Leveraging Pre-trained Models for Faster Training
- Topic 27.2: Fine-tuning Models for Specific Tasks
- Topic 27.3: Domain Adaptation Techniques
Module 28: Data Augmentation Strategies - Topic 28.1: Image Data Augmentation Techniques
- Topic 28.2: Text Data Augmentation Methods
- Topic 28.3: Augmenting Other Data Types
Module 29: Generative AI for Drug Design and Discovery - Topic 29.1: De Novo Molecular Design
- Topic 29.2: Target Identification and Validation
- Topic 29.3: Predicting Drug Interactions and Toxicity
Module 30: Generative AI for Materials Science - Topic 30.1: Designing New Materials with Desired Properties
- Topic 30.2: Optimizing Material Processing Parameters
- Topic 30.3: Predicting Material Performance and Durability
Module 31: Generative AI for Financial Trading - Topic 31.1: Algorithmic Trading Strategies
- Topic 31.2: Risk Management and Portfolio Optimization
- Topic 31.3: Fraud Detection and Prevention
Module 32: Generative AI for Climate Modeling - Topic 32.1: Generating Realistic Climate Scenarios
- Topic 32.2: Predicting the Impact of Climate Change
- Topic 32.3: Developing Sustainable Solutions
Module 33: Generative AI for Art and Music Creation - Topic 33.1: Generating Original Artwork
- Topic 33.2: Composing New Music
- Topic 33.3: Creating Interactive Art Installations
Module 34: Building a Generative AI Community - Topic 34.1: Engaging with Other Practitioners
- Topic 34.2: Sharing Knowledge and Resources
- Topic 34.3: Collaborating on Projects
Module 35: Contributing to Open Source Generative AI Projects - Topic 35.1: Finding Opportunities to Contribute
- Topic 35.2: Following Contribution Guidelines
- Topic 35.3: Giving Back to the Community
Module 36: Staying Up-to-Date with the Latest Generative AI Research - Topic 36.1: Following Key Researchers and Institutions
- Topic 36.2: Reading Research Papers
- Topic 36.3: Attending Conferences and Workshops
Module 37: Introduction to GAN Inversion - Topic 37.1: Understanding GAN Inversion Techniques
- Topic 37.2: Applications of GAN Inversion
Module 38: Exploring Style Transfer Techniques - Topic 38.1: Neural Style Transfer Methods
- Topic 38.2: Applying Style Transfer to Images and Videos
Module 39: Generative Models for Video Synthesis - Topic 39.1: Creating Realistic Video Content
- Topic 39.2: Video Editing and Manipulation
Module 40: Text-to-Image Generation with DALL-E and Stable Diffusion - Topic 40.1: Using Text Prompts to Generate Images
- Topic 40.2: Exploring Different Text-to-Image Models
Module 41: Image Super-Resolution with Generative Models - Topic 41.1: Enhancing Image Quality with AI
- Topic 41.2: Super-Resolving Low-Resolution Images
Module 42: Introduction to Generative Agents - Topic 42.1: Creating Intelligent Virtual Characters
- Topic 42.2: Developing Interactive AI Agents
Module 43: Building a Generative AI-Powered Chatbot - Topic 43.1: Training a Chatbot with Generative Models
- Topic 43.2: Deploying a Chatbot for Customer Service
Module 44: Generative AI for Code Generation - Topic 44.1: Automating Code Development
- Topic 44.2: Generating Code from Natural Language Descriptions
Module 45: Understanding Generative AI for Data Augmentation in Healthcare - Topic 45.1: Synthetic medical image generation
- Topic 45.2: Enhanced training datasets
Module 46: Exploring Generative AI for Anomaly Detection in Manufacturing - Topic 46.1: Identifying defects and irregularities
- Topic 46.2: Improving quality control processes
Module 47: Investigating Generative AI for Personalized Education - Topic 47.1: Customized learning materials
- Topic 47.2: Adaptive tutoring systems
Module 48: Analyzing Generative AI for Supply Chain Optimization - Topic 48.1: Predicting demand and optimizing logistics
- Topic 48.2: Improving efficiency and reducing costs
Module 49: Applying Generative AI for Drug Repurposing - Topic 49.1: Identifying new uses for existing drugs
- Topic 49.2: Accelerating the drug development process
Module 50: The Role of Generative AI in Personalized Medicine - Topic 50.1: Tailoring treatments to individual patients
- Topic 50.2: Improving patient outcomes
Module 51: Generative AI for Predictive Maintenance - Topic 51.1: Predicting Equipment Failure
- Topic 51.2: Optimizing Maintenance Schedules
Module 52: Generative AI for Cybersecurity Threat Detection - Topic 52.1: Identifying Malicious Activities
- Topic 52.2: Enhancing Security Measures
Module 53: Generating Realistic Training Data for Autonomous Vehicles - Topic 53.1: Simulated Driving Environments
- Topic 53.2: Enhancing the Safety and Reliability of Autonomous Systems
Module 54: Generative AI for Personalized Financial Planning - Topic 54.1: Customized Investment Strategies
- Topic 54.2: Improving Financial Outcomes
Module 55: Using Generative AI for Enhanced Customer Service Experiences - Topic 55.1: Personalized interactions
- Topic 55.2: Improving Customer Satisfaction
Module 56: Generative AI for Creating Immersive Gaming Experiences - Topic 56.1: Dynamically Generated Game Content
- Topic 56.2: Engaging and Realistic Game Worlds
Module 57: The Potential of Generative AI for Content Localization - Topic 57.1: Automatic Translation and Adaptation
- Topic 57.2: Expanding Global Reach
Module 58: Generative AI for Creating Interactive Storytelling Experiences - Topic 58.1: Branching Narratives
- Topic 58.2: Personalized Story Outcomes
Module 59: Generative AI for Virtual Reality Content Creation - Topic 59.1: Generating Immersive Environments
- Topic 59.2: Creating Engaging VR Experiences
Module 60: Building a Scalable Generative AI Platform - Topic 60.1: Infrastructure and Architecture
- Topic 60.2: Performance Optimization
Module 61: MLOps for Generative AI - Topic 61.1: Managing the Machine Learning Lifecycle
- Topic 61.2: Deployment, Monitoring and Scaling of GenAI models
Module 62: Securing GenAI Models - Topic 62.1: Data Poisoning, Prompt Hacking and other risks
- Topic 62.2: Security best practices for Generative AI
Module 63: Data Governance for GenAI - Topic 63.1: Managing training data
- Topic 63.2: Data usage policies for Generated content
Module 64: GenAI for Code Completion - Topic 64.1: Generate useful Code Snippets for various IDEs
- Topic 64.2: Code testing and Reviewing
Module 65: GenAI for Automating documentation - Topic 65.1: Documentation automation
- Topic 65.2: Generate API documentation
Module 66: GenAI for Content Moderation - Topic 66.1: Automating Content Moderation with GenAI
- Topic 66.2: Creating AI-Powered Content Moderation policies
Module 67: GenAI for Summarization - Topic 67.1: Automating Summarization with GenAI
- Topic 67.2: Meeting Minutes Summarization
Module 68: GenAI for Customer Segmentation - Topic 68.1: Automating Customer Segmentation with GenAI
- Topic 68.2: Customer Behaviour Analysis
Module 69: GenAI for Dynamic Content - Topic 69.1: Automating Dynamic Content Generation with GenAI
- Topic 69.2: Personalized product offerings
Module 70: GenAI for Brand Monitoring - Topic 70.1: Automating Brand Monitoring with GenAI
- Topic 70.2: Improving brand reputation
Module 71: GenAI for Fraud Detection - Topic 71.1: Automating Fraud Detection with GenAI
- Topic 71.2: Improve fraud protection
Module 72: GenAI for Legal Discovery - Topic 72.1: Automating Legal Discovery with GenAI
- Topic 72.2: Reducing legal risks
Module 73: GenAI for Market Research - Topic 73.1: Automating Market Research with GenAI
- Topic 73.2: Improving business strategy
Module 74: GenAI for Training - Topic 74.1: Automating Training Content Generation with GenAI
- Topic 74.2: Improving user competency
Module 75: GenAI for Translation - Topic 75.1: Automating Translation with GenAI
- Topic 75.2: Multilingual support
Module 76: GenAI for Voice Cloning - Topic 76.1: Automating Voice Cloning with GenAI
- Topic 76.2: Generate voice based product
Module 77: GenAI for Chatbots - Topic 77.1: Automating Chatbot conversation flow
- Topic 77.2: Improved customer engagement
Module 78: GenAI for Virtual Assistant - Topic 78.1: Automating Virtual Assistant workflows
- Topic 78.2: Improve operational efficiency
Module 79: GenAI for Story Telling - Topic 79.1: Automating Story Telling workflow
- Topic 79.2: Interactive storytelling
Module 80: GenAI for Game Design - Topic 80.1: Automating Game Design workflow
- Topic 80.2: Generate new game ideas
Module 81: Advanced Prompt Engineering Techniques - Topic 81.1: Chain of thought prompting
- Topic 81.2: Tree of Thoughts
Module 82: Generative AI for Synthetic Data Generation - Topic 82.1: Generate tabular Synthetic data
- Topic 82.2: Generate image and video Synthetic data
Upon successful completion of this course, you will receive a certificate issued by The Art of Service, recognizing your expertise in Generative AI.