Master Generative AI: From Theory to ChatGPT Clone

What you will learn:

Generative AI fundamentals, applications, and modalities (text-to-text, image-to-text, etc.)
ChatGPT's inner workings: GPT, LLMs, and Transformers
Latent Diffusion, Stable Diffusion, and DALL-E mechanisms
GANs and VAEs for image generation
Ethical considerations and responsible use of Generative AI
Building a ChatGPT clone with OpenAI APIs and Streamlit
Developing NLP applications using OpenAI APIs (summarization, classification, fine-tuning)
Creating NLP applications using Hugging Face Transformers (language models, summarization, translation)
Building a Midjourney-style application using OpenAI DALL-E and Stable Diffusion

Description

Embark on a transformative journey into the world of Generative AI! This comprehensive course demystifies the cutting-edge technologies shaping the future of AI interaction. Dive deep into the theoretical underpinnings of generative models, exploring various modalities such as text-to-text, image-to-text, text-to-image, and text-to-voice. We'll examine state-of-the-art models, including Transformers, LLMs like GPT (powering ChatGPT), GANs, VAEs, diffusion models (DALL-E, Stable Diffusion), and VALL-E.

Beyond theory, we'll equip you with practical skills. Build your own ChatGPT clone using OpenAI APIs, mastering GPT-3.5, ChatGPT, and DALL-E functionalities. You'll also explore Hugging Face Transformers and Stable Diffusion, developing hands-on projects. This course navigates the ethical considerations, exploring the positive and negative impacts of generative AI, preparing you for responsible innovation.

Whether you're a seasoned developer or a curious beginner, this course offers a structured path to mastering generative AI. Enroll now and become a pioneer in this rapidly evolving field!

Curriculum

Introduction

This introductory section sets the stage for your Generative AI journey. The "Introduction" lecture provides a foundational overview, while the "Course Overview" lecture details the course structure, learning objectives, and what you can expect to achieve. Expect a comprehensive introduction to the exciting world of Generative AI.

Understanding Generative AI

Gain a solid understanding of generative AI's core concepts. Lectures cover the definition of Generative AI, its distinctions from discriminative models, and the compelling reasons for using generative models. You'll learn about encoder-decoder design patterns and the various modalities involved in Generative AI, setting the basis for understanding the different types of generation models covered in subsequent sections.

Text-to-Text Generative AI

This section dives into the intricacies of text-to-text generation. Starting from basic statistical and neural language models, you'll progress to advanced architectures such as seq2seq models, attention mechanisms, and transformers. Hands-on experience with Hugging Face Transformers is provided, alongside explorations of Large Language Models (LLMs), including BERT and GPT. You'll build a ChatGPT clone using OpenAI APIs, culminating in deploying it using Streamlit. This section provides a deep dive into the technology behind cutting-edge language models such as ChatGPT.

Image-to-Image Generative AI

Explore the world of image generation with this section dedicated to image-to-image generative models. You will learn about autoencoders, variational autoencoders (VAEs), and generative adversarial networks (GANs). The practical component involves hands-on coding using Keras to build and train these models, covering various GAN architectures such as DCGANs and conditional GANs. The section concludes with discussions on advanced techniques like domain adaptation using pix2pix and CycleGAN.

Multi-Modal Generative AI

This section expands on the previous modules by exploring multi-modal generative AI, focusing on text-to-image generation. You will be introduced to diffusion models, latent diffusion models (LDMs), and CLIP, followed by practical sessions using Stable Diffusion and OpenAI's DALL-E API. You'll build a Midjourney clone application, and also learn about Image Captioning and Text-to-Voice generation using VALL-E.

Ethical Considerations: The Good, the Bad, and the Ugly

This crucial section delves into the ethical implications of generative AI. It explores the positive societal impacts, the potential risks and downsides, and how to approach this powerful technology responsibly. You will gain a balanced perspective, preparing you to navigate the complexities of this rapidly evolving field.

Conclusion

This concluding section summarizes the key takeaways of the course, reiterating the core concepts and empowering you to confidently apply your newly acquired knowledge and skills.

Course Materials

This section provides access to supplemental materials to aid your learning journey.

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