Generative AI
5-Day Intensive Course (40 Hours)
About SNA Technologies:
SNA Technologies Inc. (SNAT), established in 2006 and headquartered in Michigan, USA, is a global leader in IT solutions and training. We specialize in AI-driven training and consulting services aimed at enhancing the technical capabilities of IT professionals worldwide. As a recognized member of The Open Group, we contribute to the advancement of global standards in enterprise architecture and IT solutions. With our experienced team, we are committed to providing top-tier AI education to professionals worldwide.
Why Choose SNA?
The Generative AI course offers a deep dive into the concepts and applications of Generative AI. Participants will explore the fundamentals of Generative AI, models of Large Language Models (LLM), and advanced optimization techniques. The course also includes practical case studies to showcase real-world use cases, and introduces cutting-edge topics such as Retrieval-Augmented Generation (RAG) and Vector Databases.
Key reasons to choose SNA for your Generative AI training:
– Experienced instructors with hands-on industry experience.
– Focus on real-world applications and case studies.
– Access to advanced techniques in LLM and Generative AI optimization.
– 40 hours of in-depth training over 5 days.
– Proven track record of successful certifications.
Course Approach
The Generative AI course combines theoretical understanding with practical application. Participants will learn the foundational principles of Generative AI, explore advanced techniques, and engage in hands-on exercises that focus on real-world use cases. The course also covers the optimization of large language models (LLMs) and the fine-tuning of these models for specific applications.
Summary
The Generative AI course is designed to provide participants with a comprehensive understanding of Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), and Vector Databases. By the end of this course, learners will be equipped to apply these advanced concepts to real-world problems and optimize AI models for practical use.
Learning Goals
By the end of the course, participants will be able to:
– Understand the foundational concepts of Generative AI and its various applications.
– Work with large language models (LLM) and optimize them for different tasks.
– Implement and fine-tune LLMs for practical use cases.
– Understand and apply Retrieval-Augmented Generation (RAG) techniques.
– Explore the role of Vector Databases in generative AI applications.
Topics Covered (40 Hours)
- Basic Concepts of Generative AI
– Introduction to Generative AI and its impact on industries.
– Types of generative models (e.g., GANs, VAEs, transformers).
– Fundamental principles and methodologies in Generative AI.
- Use Cases of Generative AI
– Applications of Generative AI in various industries (e.g., content creation, gaming, healthcare).
– How businesses are leveraging Generative AI for innovation.
– Real-world examples of Generative AI solutions.
- Case Study: Generative AI Use Case
– In-depth case study analysis of a real-world Generative AI application.
– Practical insights and lessons learned from successful implementations.
– Understanding the challenges and opportunities in deploying Generative AI.
- Models of Large Language Models (LLM)
– Overview of LLMs and their applications in natural language processing (NLP).
– Exploring architectures like GPT, BERT, and T5.
– How LLMs are used to generate human-like text.
- Optimization of LLM
– Techniques for optimizing large language models for speed and performance.
– Strategies to fine-tune LLMs for specific tasks or industries.
– Optimizing LLMs for better accuracy, efficiency, and cost-effectiveness.
- Retrieval-Augmented Generation (RAG)
– Introduction to Retrieval-Augmented Generation (RAG).
– How RAG enhances the performance of generative models by integrating external data sources.
– Applications of RAG in improving the quality and relevance of AI-generated content.
- Vector Database
– Understanding the role of Vector Databases in AI.
– How Vector Databases store and retrieve high-dimensional data for AI models.
– Practical use cases and implementation of Vector Databases in generative AI.
- Fine-Tuning of LLM
– Techniques for fine-tuning LLMs for specific use cases.
– Adapting models to improve accuracy and relevance.
– Hands-on exercises in fine-tuning LLMs for customized applications.
- Introduction to Large Action Models
– Exploring the concept of Large Action Models (LAM) and their use in AI systems.
– Applications of LAM in decision-making and real-time action generation.
– Practical examples and exercises using LAM for different AI tasks.
What Attendees Get
Course registration includes:
– Comprehensive course materials and workbooks.
– Practical exercises and projects with real-world applications.
– Access to a community of professionals and experts.
– Certificate of Completion upon successful course completion.
Who Will Benefit?
This course is ideal for:
– AI developers and data scientists looking to deepen their knowledge of Generative AI.
– IT professionals and engineers interested in advanced AI techniques.
– Researchers and technologists working with natural language processing and AI content generation.
– Professionals with a background in machine learning, AI, or software development who wish to explore Generative AI in-depth.
Prerequisites
No formal prerequisites are required, but familiarity with basic AI concepts, machine learning, and Python programming is recommended. A background in AI or data science will help you gain the most from this course.
Professional Development Units (PDUs)
Participants may be eligible to apply for PDUs towards continuing education requirements with relevant certification bodies.
Course Features
- Lectures 0
- Quizzes 0
- Duration 5 days
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
