The Truth About LLMs: From Billions of Parameters to Self Attention скачать в хорошем качестве

The Truth About LLMs: From Billions of Parameters to Self Attention

There is incredible buzz around Large Language Models (LLMs) right now because they generate text that feels remarkably human. But how does an algorithm actually learn to talk? In this video, we break down the fascinating process behind the magic to show you what is really happening under the hood. What we cover in this video: • The Basics of Prediction: Learn how LLMs function as algorithms trained on mind-boggling amounts of data to predict the most likely next word in a sentence. • Defining "Large": We explain why size isn't about physical dimensions—it’s about the insane number of adjustable parameters inside the neural network. • The Fine-Tuning Process: Discover how billions of tiny "knobs" (parameters) are tweaked and fine-tuned during training to capture every nuance of language. • The Transformer Revolution: We discuss the 2017 breakthrough paper "Attention Is All You Need" and the self-attention mechanism that finally allowed models to track context over long paragraphs. • Industry Titans: A look at the major players built on this architecture, including OpenAI’s GPT series (with over 175 billion parameters), Google’s BERT and Gemini, and Meta’s Llama 3. The Key Takeaway: Despite the realistic conversations, LLMs do not truly understand language or possess consciousness. They are simply sophisticated masters of statistical pattern matching. #LLM #FineTuning #ArtificialIntelligence #MachineLearning #TransformerArchitecture #OpenAI #GoogleGemini #Llama3 #TechEducation #DeepLearning