A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.
This article explains Monte Carlo methods for reinforcement learning, compares model‑free and model‑based approaches, details V‑ and Q‑function estimation using a Blackjack example, and discusses exploration‑exploitation trade‑offs and practical advantages of MC algorithms.
This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.
This article introduces the fundamental concepts of reinforcement learning, covering the agent‑environment interaction, key terminology, reward structures, task types, policies, value functions, the Bellman equations, and how optimal strategies are derived and approximated in practice.
This article explains the fundamental principles of diffusion models, using physics and machine‑learning analogies to describe forward and reverse diffusion, the role of Gaussian noise, iteration trade‑offs, U‑Net architecture, and shared‑weight training for image generation.
The article explains the core concepts and trade‑offs of five parallelism techniques—data, tensor, context, pipeline, and expert parallelism—plus the ZeRO optimizer, showing when each method is appropriate for training ultra‑large PyTorch models and providing concrete code snippets and performance considerations.
This article explains the Kullback‑Leibler (KL) divergence for discrete and continuous distributions, outlines its non‑negativity and asymmetry, walks through a uniform‑distribution example, provides a simple Python demonstration, and discusses key applications in variational autoencoders, reinforcement‑learning policy optimization, and other machine‑learning contexts.
This article explains the Mixture of Experts (MoE) technique used in modern LLMs, detailing its core components—experts and router—comparing dense and sparse layers, describing load‑balancing, expert capacity, and routing strategies, and showcasing real‑world examples such as Switch Transformer, Vision‑MoE, and Mixtral 8x7B.
This article introduces five core workflow design patterns for AI agents—Prompt Chaining, Routing, Parallelization, Orchestrator‑Worker, and Evaluator‑Optimizer—explaining their mechanics, concrete examples, suitable scenarios, and how they help build reliable, maintainable LLM‑driven systems.
The article surveys dozens of recent open‑source AI agent frameworks—including CrewAI, AutoGen, LangGraph, Agno, SmolAgents, Mastra, PydanticAI and Atomic Agents—explaining their core functions, design philosophies, common features such as prompt engineering and tool integration, and highlighting each framework’s strengths, limitations, and suitable use cases.
DeepSeek‑Prover‑V2, an open‑source LLM specialized for Lean 4, bridges intuitive high‑level reasoning and strict formal verification through sub‑goal decomposition, dual operation modes, and a novel cold‑start data pipeline, achieving state‑of‑the‑art results on MiniF2F, PutnamBench and CombiBench while highlighting trade‑offs in inference cost and model scalability.
