Certified Generative AI Specialist (CGAI)

Description:

The Certified Generative AI Specialist (CGAI) course is a comprehensive program designed to equip participants with a deep understanding of Generative AI technologies, tools, and applications. Participants will gain hands-on experience with cutting-edge AI models such as GPT, Stable Diffusion, and other generative frameworks. The course covers foundational concepts, advanced techniques, and real-world applications in industries like content creation, image generation, and automation.

By the end of this course, participants will be prepared to design, implement, and fine-tune Generative AI models for various applications and understand ethical considerations and deployment challenges in the field.

For Certification based Assistance and Mock quizzes please visit: https://certify360.ai/

Duration: 5 Days

Course Code: BDT407

Learning Objectives:

Upon completion of this course, participants will:

1. Understand the fundamentals of Generative AI and how it differs from traditional AI
2. Learn about prominent generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformers.
3. Gain hands-on experience building and deploying generative models using frameworks like TensorFlow, PyTorch, and Hugging Face.
4. Explore applications of generative AI, including text, image, video, and audio
5. Analyze ethical and societal implications of Generative AI, including bias, copyright concerns, and misuse.
6. Implement fine-tuning of pre-trained models to create domain-specific
7. Learn deployment best practices and optimization techniques for Generative AI

• Basic understanding of Python programming
• Familiarity with machine learning concepts and frameworks (e.g., TensorFlow, PyTorch)
• Knowledge of mathematics concepts such as linear algebra and probability

• Data Scientists and AI Engineers
• Machine Learning Developers
• Research Scientists
• Software Engineers interested in AI
• Professionals looking to specialize in Generative AI technologies
• Enthusiasts aiming to explore emerging AI applications like generative text, image synthesis, and large language models

Course Outline:

Module 1: Introduction to Generative AI

● What is Generative AI?
● Evolution of Generative AI: From GANs to GPT
● Key Applications of Generative AI
● Overview of Generative AI Models
● Ethical Considerations in Generative AI

Hands-On:

● Introduction to Google Colab and setting up the environment
● Exploring pre-trained models from Hugging Face

Module 2: Foundations of Generative Models

● Generative Adversarial Networks (GANs): Architecture and Applications
● Variational Autoencoders (VAEs): Understanding Probabilistic Models
● Transformers: Attention Mechanisms and Sequence-to-Sequence Models
● Large Language Models (LLMs) like GPT, BERT, and ChatGPT

Hands-On:

● Building a simple GAN for image generation
● Training a VAE for data compression
● Implementing a text generation model using GPT

Module 3: Advanced Techniques in Generative AI

● Fine-Tuning Pre-Trained Models for Domain-Specific Use Cases
● Transfer Learning in Generative AI
● Prompt Engineering for Large Language Models
● Zero-Shot and Few-Shot Learning with Generative AI Models

Hands-On:

● Fine-tuning GPT for a custom dataset (e.g., FAQs or domain-specific text)
● Exploring Stable Diffusion for custom image generation

Module 4: Applications of Generative AI

● Text Generation: Content creation, summarization, and code generation

● Image Synthesis: Style transfer, image inpainting, and art generation
● Audio and Video Generation: Speech synthesis, voice cloning, and video generation
● Generative AI in Automation and Decision-Making

Hands-On:

● Creating AI-generated artwork using Stable Diffusion or DALL-E
● Building a text-to-speech model using Tacotron or WaveNet
● Generating AI-powered chatbots for specific industries

Module 5: Deployment and Optimization

● Deploying Generative AI Models in Production
● Model Optimization: Pruning, Quantization, and Distillation
● Monitoring and Managing Generative AI Systems
● Addressing Bias and Ethical Concerns in Generative AI

Hands-On:

● Deploying a generative model on a cloud platform (AWS, Azure, or GCP)
● Optimizing models for faster inference on edge devices

Module 6: Capstone Project

Participants will work on a comprehensive project to integrate the concepts and techniques learned during the course.
Capstone Project Examples:

● Building a domain-specific chatbot for customer support
● Generating AI-powered marketing visuals and text content

● Designing an AI model to generate synthetic audio for voiceovers

Training material provided: Yes (Digital format)

Any Additional Information

Any additional information about Labs / Software Installs required for the course