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Generative AI Application Development

Comprehensive course for developer to start developing generative AI base application with practical study cases

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Training Objectives

This course aims to equip developers with the foundational knowledge and hands-on skills necessary to build generative AI applications. Through practical case studies, participants will learn to design, develop, and deploy AI-driven solutions effectively.

Target Participants

This training is designed for software developers, data scientists, and AI enthusiasts with intermediate programming experience and a basic understanding of machine learning concepts.

Standard Modules to Be Covered

  • Introduction to Generative AI and Core Concepts
  • Overview of Popular Generative Models (e.g., GPT, DALL-E, Stable Diffusion)
  • Setting Up Development Environments for AI Projects
  • Data Preparation and Preprocessing Techniques
  • Model Training, Fine-Tuning, and Evaluation
  • Integrating Generative AI into Applications via APIs
  • Ethical Considerations and Best Practices in AI Development
  • Case Studies: Real-World Applications and Problem-Solving

Additional Materials

  • Access to Pre-configured Cloud Development Environments
  • Downloadable Code Repositories and Sample Datasets
  • Comprehensive Reading List and Research Papers
  • Cheat Sheets for Model Parameters and API Usage
  • Video Tutorials for Complex Module Walkthroughs

Training Methods

  • Interactive Lectures with Live Demonstrations
  • Hands-On Labs and Coding Exercises
  • Group Projects Simulating Real-World Scenarios
  • Peer Code Reviews and Collaborative Problem-Solving
  • Instructor-Led Q&A and Mentoring Sessions

Expected Outcomes

Upon completion, participants will be able to independently develop generative AI applications, apply ethical AI principles, utilize industry-standard tools and frameworks, and implement solutions based on practical case studies to solve business challenges.

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What you will learn?

Introduction to Generative AI

  • 1. What is Generative AI?

  • 2. History and Evolution

  • 3. Key Concepts and Terminology

  • 4. Types of Generative Models

  • 5. Real-World Applications Overview

  • 6. Ethical Considerations

Foundational Technologies

  • 1. Neural Networks Basics

  • 2. Deep Learning Architectures

  • 3. Transformers and Attention Mechanisms

  • 4. Diffusion Models

  • 5. Generative Adversarial Networks (GANs)

  • 6. Variational Autoencoders (VAEs)

  • 7. Autoregressive Models

Development Environment Setup

  • 1. Hardware Requirements

  • 2. Cloud Platforms Comparison

  • 3. Local Development Setup

  • 4. Essential Libraries and Frameworks

  • 5. Version Control for AI Projects

  • 6. Development Best Practices

Text Generation Applications

  • 1. Language Models Fundamentals

  • 2. Prompt Engineering Techniques

  • 3. Chatbot Development

  • 4. Content Creation Systems

  • 5. Code Generation Tools

  • 6. Text Summarization

  • 7. Sentiment Analysis Integration

Image Generation Projects

  • 1. Image Generation Models Overview

  • 2. Style Transfer Implementation

  • 3. Image-to-Image Translation

  • 4. Super-Resolution Applications

  • 5. Creative Design Tools

  • 6. Medical Imaging Applications

  • 7. Quality Assessment Methods

Audio and Music Generation

  • 1. Audio Synthesis Models

  • 2. Music Composition Systems

  • 3. Voice Generation and Cloning

  • 4. Audio Enhancement Tools

  • 5. Podcast Creation Applications

  • 6. Sound Effect Generation

  • 7. Real-time Audio Processing

Multimodal Applications

  • 1. Understanding Multimodal AI

  • 2. Text-to-Image Systems

  • 3. Image Captioning

  • 4. Video Generation

  • 5. Cross-Modal Retrieval

  • 6. Interactive Media Applications

  • 7. Augmented Reality Integration

Deployment and Scaling

  • 1. Model Optimization Techniques

  • 2. API Development and Management

  • 3. Containerization Strategies

  • 4. Cloud Deployment Options

  • 5. Monitoring and Maintenance

  • 6. Cost Optimization

  • 7. Security Best Practices

Case Study: E-commerce Assistant

  • 1. Project Requirements Analysis

  • 2. Chatbot Architecture Design

  • 3. Product Description Generation

  • 4. Customer Support Automation

  • 5. Personalization Features

  • 6. Integration with Existing Systems

  • 7. Performance Evaluation

Case Study: Creative Content Studio

  • 1. Business Use Case Definition

  • 2. Multi-Modal Content Generation

  • 3. Brand Consistency Maintenance

  • 4. Workflow Automation

  • 5. Quality Control Systems

  • 6. Team Collaboration Features

  • 7. ROI Measurement

Case Study: Educational Platform

  • 1. Learning Content Generation

  • 2. Adaptive Learning Systems

  • 3. Assessment Creation

  • 4. Student Interaction Tools

  • 5. Progress Tracking

  • 6. Content Personalization

  • 7. Implementation Challenges

Advanced Topics and Future Trends

  • 1. Federated Learning for Privacy

  • 2. Explainable AI in Generative Models

  • 3. Edge Computing Applications

  • 4. Quantum Computing Implications

  • 5. Emerging Model Architectures

  • 6. Industry-Specific Applications

  • 7. Career Development Pathways

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