[100% Off] Certified Generative Ai &Amp; Transformers
Generative AI & Transformers: Master LLMs, Diffusion Models, PyTorch Implementation, and Certification Preparation.
What you’ll learn
- Comprehend the fundamental architecture of the Transformer model
- including the self-attention mechanism and positional encoding.
- Design and implement custom Large Language Models (LLMs) using modern deep learning frameworks like PyTorch or TensorFlow.
- Effectively apply advanced prompt engineering techniques to maximize performance and safety of pre-trained LLMs.
- Master fine-tuning strategies
- including parameter-efficient methods (LoRA
- QLoRA)
- for adapting LLMs to specific tasks efficiently.
- Build and deploy Retrieval-Augmented Generation (RAG) systems for enhanced factual grounding and enterprise AI applications.
Requirements
- Solid understanding of Python programming (intermediate level or higher)
- Familiarity with foundational Machine Learning concepts (training
- validation
- metrics)
- Basic experience with deep learning libraries such as PyTorch or TensorFlow
- Experience working with PyTorch or TensorFlow/Keras framework basics.
Description
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.
Decoding the Transformer Architecture 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.
Mastering Generative Models 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.
Practical Deployment & Certification Focus This course emphasizes practical deployment using the powerful Hugging Face ecosystem. You will learn how to select, optimize, and deploy models efficiently in production environments. By the end, you will possess a certification-ready understanding of Generative AI, positioning you as a leading expert in this rapidly expanding field. This course bridges the gap between theoretical knowledge and real-world deployment challenges.
