Post Article 36: Large Language Models (LLMs) and Generative AI 🤖🚀

Article 36: Large Language Models (LLMs) and Generative AI 🤖🚀 Generative AI refers to models that create new content like text, images or audio rather than just classifying existing data. LLMs are Decoder only Transformers trained on massive textual datasets to predict and generate text. 1. How LLMs Work (Next-Token Prediction) 🔮 At its core, an LLM performs a probability calculation, given a sequence of words (tokens), it predicts the most likely next token. more... 2. The Training Pipeline of an LLM 🏗 Building a production-grade LLM involves three major phases I. Pre-training (Unsupervised / Self-Supervised) II. Supervised Fine-Tuning (SFT) III. Alignment (RLHF & DPO) 3. Key Paradigms in Modern Generative AI ✨ Retrieval-Augmented Generation (RAG) Diffusion Models (Image Generation) Mixture of Experts (MoE) Summary 📝📚 LLMs utilize Decoder-based Transformer architectures to predict text sequentially. They undergo Pre-training, Supervised Fine-Tuning (SFT), and Alignment (RLHF/DPO) to become instruction-following assistants. Frameworks like RAG and architectural techniques like Mixture of Experts (MoE) extend these models into production systems. 💬🤝🚀🌍 Congratulations, we have completed the entire A to Z Machine Learning and AI Roadmap from fundamental statistics to modern Generative AI models now. 🗺🤖 ✍️ @TheInfinityAI