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Understanding the Basics of Generative AI Models

Fundamental Concepts

Probability Distributions

Latent Variables

Data Generation Process

Key Techniques in Generative AI

Variational Autoencoders (VAEs)

Generative Adversarial Networks (GANs)

Transformer Models

Vector Databases

RAG: Retrieval Augmented Generation

Extending LLMs with Function Calling

Evaluation: How to tell if the AI is doing what you think it should be doing

Deep Dive into Generative AI Models

Detailed Study of Variational Autoencoders (VAEs)

Understanding the Architecture

VAEs Training Process

Applications of VAEs

Detailed Study of Generative Adversarial Networks (GANs)

GANs Training Process

Applications of GANs

Detailed Study of Transformer Models

Transformer Models Training Process

Applications of Transformer Models

Architecture of AI powered applications


Practical Applications and Future Trends of Generative AI Models

Real-world Use Cases

Current Challenges and Solutions

Future Developments and Opportunities in Generative AI Models

How to integrate AI into existing applications


The landscape of tools and libraries of building AI powered applications


Quickstart: Creating a “Hello World” application for Generative AI in just minutes


Prompt Engineering Techniques using Prompt Templating and Roles


Mapping AI output to POJOs


Implementing RAG (Retrieval Augmented Generation)


Exploring Function calling: Enable the AI to access APIs on demand


Evaluation Driven Development.


Using Spring AI with GraalVM

Conclusion

Recap of Key Learnings

Encouragement for Further Study and Exploration

Introduction to Generative AI Models


A Concise History of AI/ML
Introduction to Generative AI Models
Prompt Engineering Techniques & Best Practices
Vector Databases
RAG: Retrieval Augmented Generation
Extending LLMs with Function Calling
Evaluation: How to tell if the AI is doing what you think it should be doing
Architecture of AI powered applications
How to integrate AI into existing applications
The landscape of tools and libraries of building AI powered applications
HANDS ON CODE EXERCISES WITH SPRING AI:
Quickstart: Creating a “Hello World” application for Generative AI in just minutes
Prompt Engineering Techniques using Prompt Templating and Roles
Mapping AI output to POJOs
Implementing RAG (Retrieval Augmented Generation)
Exploring Function calling: Enable the AI to access APIs on demand
Evaluation Driven Development.
Using Spring AI with GraalVM

Welcome to the 'Introduction to Generative AI Models' course! In this comprehensive learning experience, we will explore the exciting world of generative artificial intelligence, focusing on the fundamental concepts, key techniques, and practical applications that are driving the field forward.

The course begins by introducing the basics of generative AI models, providing you with a solid understanding of the underlying principles that power these state-of-the-art systems. We will dive into fundamental concepts such as probability distributions, latent variables, and the data generation process. Additionally, you will learn about key techniques in generative AI, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformer Models, Vector Databases, RAG (Retrieval Augmented Generation), and extending Language Models with Function Calling.

As we delve deeper into the subject, we will conduct a detailed study of Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer Models, examining their architecture, training processes, and applications. You will gain hands-on experience in implementing and applying these powerful techniques to real-world challenges and scenarios.

In the latter part of the course, we will shift our focus to practical applications and future trends of generative AI models. We will explore various use cases, discuss current challenges and solutions, and learn about future developments and opportunities in the field. Furthermore, we will cover essential topics such as integrating AI into existing applications, leveraging the landscape of tools and libraries for building AI-powered applications, and employing evaluation-driven development techniques to ensure your AI models are performing as expected.

Throughout the course, you will have the opportunity to put your knowledge into practice by creating a "Hello World" application for Generative AI in just minutes, mastering prompt engineering techniques, implementing RAG and function calling, and mapping AI output to POJOs (Plain Old Java Objects).

By the end of this course, you will have a deep understanding of generative AI models, their applications, and the tools and techniques required to build and integrate these powerful systems into your projects. You will be well-equipped to explore further and contribute to the rapidly evolving field of artificial intelligence. So, let's embark on this exciting journey together!