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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advance-llms-ai-agents-mcp-rl-interview-qa-tests/
课程评论：没有评论
This Coursera course, "Advance LLMs, AI Agents, MCP & RL - Interview Q & A / Tests," offers comprehensive preparation for aspiring AI engineers specializing in advanced Large Language Models (LLMs), AI agents, and Reinforcement Learning (RL). The course is designed around practice tests and interview questions mirroring recent AI breakthroughs, covering a wide array of cutting-edge topics. Key areas explored include: * **LLM Pre-Training:** Data curation, scaling laws, decoding techniques, KV caching, learning rate schedulers, adapters, and Fully Sharded Data Parallelism (FSDP). * **LLM Architectures & Optimization:** Advanced components like Flash Attention and Mixture of Experts (MoE), along with evaluation tools such as LLM Judges and Self-Instruct frameworks. * **Fine-Tuning & Alignment:** Techniques including GRPO, PPO, DPO, DAPO, REINFORCE with Leave-One-Out (RLOO), RLHF, and ReAct-style tool-augmented fine-tuning. * **Specialized Frameworks:** LangGraph for agentic AI workflows, Model Context Protocol (MCP) for dynamic tool integration, and DeepSeek models with multi-stage training. * **Model Compression & Inference:** Knowledge distillation, quantization methods, and test-time scaling for efficient deployment. * **Retrieval-Augmented Generation (RAG):** Focus on RAG triad (context relevance, groundedness, answer relevance) for truthful and context-aware responses. * **LLM Evaluation & Tooling:** Systematic evaluation methods, reward modeling, rejection sampling, and human-in-the-loop strategies. * **Infrastructure & Deployment:** Modern GPU architectures and performance optimization for training and inference. The course also delves into practical interview questions from top tech companies, providing insights into areas like PPO's early stopping, DPO's advantages over PPO, reward vs. value functions, quantization differences, ReAct dataset components, memory retention, GRPO's novelty and variance reduction, ZeRO optimizer principles, MoE gating, Flash Attention's effectiveness, AdamW's benefits, contrastive loss training, the role of LLM-as-a-Judge, and the advantage of KV caching.
Our expertly crafted practice tests are aligned with the most recent breakthroughs in AI, ensuring both comprehensive coverage and focused depth on high-impact areas. These tests are tailored to assess your understanding across a wide spectrum of cutting-edge topics including:· LLM Pre-Training: Data curation strategies, scaling laws, decoding techniques, KV caching, learning rate schedulers, adapters, and fully sharded data parallelism (FSDP).· LLM Architectures & Optimization: Advanced components like Flash Attention, Mixture of Experts (MoE), Switch Transformers, and evaluation tools such as LLM Judges and Self-Instruct frameworks.· Fine-Tuning & Alignment: Techniques such as GRPO, PPO, DPO, DAPO, REINFORCE with Leave-One-Out (RLOO), RLHF, and ReAct-style tool-augmented finetuning.· Specialized Frameworks: LangGraph for agentic AI workflows, Model Context Protocol (MCP) for dynamic tool integration, and DeepSeek models with multi-stage training pipelines including reinforcement learning and distillation.· Model Compression & Inference: Knowledge distillation, quantization (including advanced methods like 2-bit packing), and test-time scaling for cost-efficient deployment.· Retrieval-Augmented Generation (RAG): Deep understanding of the RAG triad-context relevance, groundedness, and answer relevance-and their role in evaluating truthful and context-aware responses.· LLM Evaluation & Tooling: Techniques for systematic evaluation of model outputs, reward modeling, rejection sampling, and human-in-the-loop strategies.· Infrastructure & Deployment: Insight into modern GPU architectures and performance optimization for pretraining and inference workloads.Additionally, the course includes real-world interview questions from top-tier tech companies, making it ideal for aspiring AI engineers aiming to master LLMs, agentic systems, and RL-based fine-tuning at scale.Sample Questions:Why does PPO sometimes use early stopping based on KL divergence?What core advantage does Direct Preference Optimization (DPO) offer over PPO in training language models?What distinguishes a reward function from a value function?What is an advantage of per-channel quantization over per-tensor quantization?In a ReAct dataset, what does the 'Observation' field typically represent?Which type of memory retains knowledge across multiple threads and invocations?How does GRPO differ from traditional reinforcement learning approaches?Why does GRPO reduce variance in policy gradient updates?What is the main principle behind ZeRO optimizer?What does gating in MoE determine?Why is Flash Attention effective for transformer-based models?What is the primary advantage of AdamW over Adam for LLM pretraining?What is typically required to train a contrastive loss-based model?What is the role of LLM-as-a-Judge in combining self-instruct and self-reward?What is the main advantage of KV caching in decoding?
