[ { "slug": "search-pathfinding", "icon": "🧭", "category": "search", "category_en": "Search", "category_zh": "搜索", "title": "Pathfinding Visualizer", "title_zh": "寻路可视化", "desc": "Compare breadth-first search (BFS), depth-first search (DFS), Dijkstra’s algorithm, and A* on the same grid.", "desc_zh": "在同一网格上比较广度优先搜索（BFS）、深度优先搜索（DFS）、Dijkstra 算法和 A*。", "time": "20 min", "level": "start", "featured": "Race A* against BFS on the same weighted maze.", "featured_zh": "让 A* 与 BFS 在同一个加权迷宫中竞速。", "accent": "#2563eb", "course_order": 1, "showcase_order": 1, "course_phase": "Search and problem solving", "accent_name": "Cobalt" }, { "slug": "bayes-classifier", "icon": "🎯", "category": "probability", "category_en": "Probability", "category_zh": "概率", "title": "Bayes Rule Playground", "title_zh": "贝叶斯定理演练场", "desc": "Change the base rate and test quality to see why high accuracy can still produce a surprising posterior.", "desc_zh": "改变基率和检测质量，观察高准确率为什么仍会产生意外的后验概率。", "time": "20 min", "level": "start", "featured": "Compare the same test under rare and common conditions.", "featured_zh": "比较同一检测在罕见和常见条件下的结果。", "accent": "#a21caf", "course_order": 5, "showcase_order": 2, "course_phase": "Probability", "accent_name": "Fuchsia" }, { "slug": "overfitting", "icon": "📈", "category": "ml", "category_en": "Model evaluation", "category_zh": "模型评估", "title": "Overfitting Explorer", "title_zh": "过拟合探索器", "desc": "Raise model complexity and compare training error with test error on fresh samples.", "desc_zh": "提高模型复杂度，并比较训练误差与新样本上的测试误差。", "time": "25 min", "level": "start", "featured": "Find the point where a better fit becomes a worse model.", "featured_zh": "找到拟合更好却模型更差的转折点。", "accent": "#047857", "course_order": 8, "showcase_order": 3, "course_phase": "Machine learning and evaluation", "accent_name": "Emerald" }, { "slug": "q-learning-gridworld", "icon": "🤖", "category": "rl", "category_en": "Reinforcement learning", "category_zh": "强化学习", "title": "Q-Learning Gridworld", "title_zh": "Q 学习网格世界", "desc": "Train by trial and error, inspect value backups, and compare exploration schedules on the same world.", "desc_zh": "通过试错训练，检查价值回传，并在同一世界上比较探索策略。", "time": "30 min", "level": "advanced", "featured": "Compare an early policy with one learned from longer experience.", "featured_zh": "比较早期策略与经过更长经验学习的策略。", "accent": "#b91c1c", "course_order": 12, "showcase_order": 4, "course_phase": "Reinforcement learning", "accent_name": "Crimson" }, { "slug": "wumpus-world", "icon": "🕳️", "category": "logic", "category_en": "Logical agents", "category_zh": "逻辑智能体", "title": "Wumpus World", "title_zh": "Wumpus 世界", "desc": "Follow a logical agent as percepts become safe-cell conclusions, unresolved risks, and forced choices.", "desc_zh": "观察逻辑智能体如何把感知转化为安全结论、未解决风险和被迫选择。", "time": "25 min", "level": "core", "featured": "Separate a square that is unknown from one proven safe.", "featured_zh": "区分未知格子与已证明安全的格子。", "accent": "#c2410c", "course_order": 3, "showcase_order": 5, "course_phase": "Logic and knowledge", "accent_name": "Burnt orange" }, { "slug": "cnf-sat", "icon": "🧩", "category": "logic", "category_en": "Logic and SAT", "category_zh": "逻辑与 SAT", "title": "CNF and SAT Builder", "title_zh": "CNF 与 SAT 构建器", "desc": "Rewrite propositions into conjunctive normal form (CNF), then watch a satisfiability solver prune assignments.", "desc_zh": "把命题改写为合取范式（CNF），再观察可满足性求解器如何剪枝赋值。", "time": "25 min", "level": "core", "featured": "Compare a satisfiable formula with a forced contradiction.", "featured_zh": "比较一个可满足公式与一个必然矛盾。", "accent": "#6d28d9", "course_order": 4, "showcase_order": 6, "course_phase": "Logic and knowledge", "accent_name": "Violet" }, { "slug": "bayes-network", "icon": "🔗", "category": "probability", "category_en": "Probabilistic inference", "category_zh": "概率推理", "title": "Bayesian Network", "title_zh": "贝叶斯网络", "desc": "Edit evidence, compare exact inference with sampling, and see explaining away in a causal graph.", "desc_zh": "编辑证据，比较精确推理与采样，并在因果图中观察解释消除。", "time": "30 min", "level": "core", "featured": "Observe how evidence for one cause can lower belief in another.", "featured_zh": "观察一个原因的证据如何降低对另一个原因的信念。", "accent": "#0f766e", "course_order": 6, "showcase_order": 7, "course_phase": "Probabilistic reasoning", "accent_name": "Teal" }, { "slug": "knn-classifier", "icon": "🗳️", "category": "ml", "category_en": "Classification", "category_zh": "分类", "title": "K-Nearest Neighbors", "title_zh": "K 最近邻", "desc": "Move a query point, change k and vote weighting, and inspect every neighbor that determines the class.", "desc_zh": "移动查询点，改变 k 和投票权重，并检查决定类别的每个邻居。", "time": "20 min", "level": "start", "featured": "Compare k=1 with a larger vote near a noisy boundary.", "featured_zh": "在有噪声的边界附近比较 k=1 与更大的投票范围。", "accent": "#be123c", "course_order": 7, "showcase_order": 8, "course_phase": "Machine learning", "accent_name": "Rose" }, { "slug": "hill-climbing", "icon": "⛰️", "category": "search", "category_en": "Local search", "category_zh": "局部搜索", "title": "Hill Climbing and Simulated Annealing", "title_zh": "爬山法与模拟退火", "desc": "Compare hill climbing with simulated annealing on the same optimization problem and starting state.", "desc_zh": "在同一优化问题和起始状态上比较爬山法与模拟退火。", "time": "25 min", "level": "core", "featured": "Use one start state to expose the difference between climbing and escaping.", "featured_zh": "用同一起点揭示上升与逃逸之间的区别。", "accent": "#a16207", "course_order": 2, "showcase_order": 9, "course_phase": "Search and optimization", "accent_name": "Ochre" }, { "slug": "neural-network", "icon": "🧠", "category": "neural", "category_en": "Neural networks", "category_zh": "神经网络", "title": "Tiny Neural Network", "title_zh": "微型神经网络", "desc": "Train a compact network and compare linear composition with a nonlinear hidden representation.", "desc_zh": "训练一个小型网络，并比较线性组合与非线性隐藏表示。", "time": "30 min", "level": "advanced", "featured": "Toggle the activation to see what the hidden layer can actually represent.", "featured_zh": "切换激活函数，观察隐藏层真正能表示什么。", "accent": "#4338ca", "course_order": 9, "showcase_order": 10, "course_phase": "Neural networks", "accent_name": "Indigo" }, { "slug": "kmeans", "icon": "🟣", "category": "ml", "category_en": "Clustering", "category_zh": "聚类", "title": "K-Means Clustering", "title_zh": "K 均值聚类", "desc": "Change k and initialization, then watch assignment and center updates reshape the clusters.", "desc_zh": "改变 k 和初始化方式，观察分配与中心更新如何重塑聚类。", "time": "25 min", "level": "core", "featured": "Run the same data from two initializations and compare the final clusters.", "featured_zh": "用两种初始化运行同一数据并比较最终聚类。", "accent": "#4d7c0f", "course_order": 10, "showcase_order": 11, "course_phase": "Unsupervised learning", "accent_name": "Olive" }, { "slug": "convolution", "icon": "🖼️", "category": "vision", "category_en": "Computer vision", "category_zh": "计算机视觉", "title": "Convolution Playground", "title_zh": "卷积演练场", "desc": "Slide kernels across an image, inspect each multiply-and-sum, and compare hand-built with learned filters.", "desc_zh": "让卷积核滑过图像，检查每次乘加，并比较手工滤波器与学习滤波器。", "time": "30 min", "level": "advanced", "featured": "Apply edge, blur, and learned kernels to the same image.", "featured_zh": "在同一图像上应用边缘、模糊和学习卷积核。", "accent": "#0e7490", "course_order": 11, "showcase_order": 12, "course_phase": "Computer vision", "accent_name": "Cyan" } ]