• Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more
  
• Use APIs and Python to fine-tune and customize LLMs for your requirements
  
• Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents
  
• Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting
  
• Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI
  
• Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets
  
• Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5
  
• Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind
  
• Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks
  

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.