Unleash Creativity with Generative AI & Transformers

Explore this Free Udemy Course on Generative AI & Transformers. Enroll now!

Welcome to the Certified Generative AI & Transformers course, designed to transform you into a professional capable of understanding, building, and deploying the most impactful AI models of the modern era. This comprehensive curriculum dives deep into the technology powering ChatGPT, Midjourney, and other revolutionary applications.

The cornerstone of this course is the deep dive into the Transformer architecture. You will not only understand the Encoder and Decoder blocks but also implement the core logic of the revolutionary Self-Attention mechanism and Multi-Head Attention from scratch. We meticulously compare and contrast variations like BERT (Encoder-only) and GPT (Decoder-only), providing you with a rock-solid theoretical foundation essential for innovation.

We move beyond theory into cutting-edge applications. You will learn practical skills for working with state-of-the-art Large Language Models (LLMs), including advanced prompt engineering techniques, efficient fine-tuning strategies (like LoRA), and model quantization for faster inference. The course also dedicates significant time to understanding multimodal Generative AI, specifically covering the mechanics and application of Diffusion Models for text-to-image generation. This course emphasizes practical deployment using the powerful Hugging Face ecosystem. By the end, you will possess a certification-ready understanding of Generative AI, positioning you as a leading expert in this rapidly expanding field.

What you will learn:

  • Understand the Transformer architecture and its components
  • Implement advanced prompt engineering techniques for LLMs
  • Deploy Generative AI models using the Hugging Face ecosystem

Course Content:

  • Sections: 3
  • Lectures: 15
  • Duration: 12 hours

Requirements:

  • Basic understanding of Python programming
  • Familiarity with machine learning concepts

Who is it for?

  • Data Scientists aiming to specialize in Natural Language Processing (NLP) and Generative Modeling.
  • Machine Learning Engineers looking to implement and deploy custom, production-grade Transformer models.
  • Deep Learning Practitioners seeking to master the attention mechanism and its latest architectural variations.
  • Software Developers transitioning into highly specialized AI/ML roles focusing on foundation models.

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