这个时代由学习型机器驱动，然而学习本身却隐藏在 API 之后。在这里它一览无余，每一步都呈现在屏幕上，并完全在你的浏览器中运行：一个神经网络实时勾勒出决策边界，一个智能体偶然撞上奖励，注意力决定该看哪个词，噪声在扩散模型下逐渐凝结成结构。没有云端，没有密钥——只有梯度、策略与概率，在发生的瞬间被绘制出来。
Watch real t-SNE run live: a random fog of high-dimensional points pulls itself apart frame by frame, gradient-descending the KL divergence until clean, separated clusters bloom — Gaussian blobs, interlocking rings, baked digits, concentric shells.
Watch real Principal Component Analysis fold a high-dimensional cloud — a tilted 3D Gaussian, the classic Iris flowers, a Swiss-roll, clustered digits — onto its principal axes, points sliding into place while the explained-variance bars fill.
Train a real multinomial Naive Bayes spam filter in your browser, then type a message and watch each word tug the verdict toward spam or ham — with the log-odds math drawn live, word by word.
Watch a population of genomes evolve over generations — selection, crossover and mutation — as a recognizable image rises from noise, rockets thread an obstacle, a phrase assembles itself, or a swarm climbs a rugged fitness landscape.
Watch a real optimizer descend a 3D loss surface — switch between SGD, Momentum, RMSProp and Adam, then crank the learning rate until it visibly diverges.
Drop labelled points on a plane and watch four real classifiers — k-NN, logistic regression, a live-trained neural net, and an RBF kernel — carve the space into coloured decision regions before your eyes.
Slide a 3×3 convolution kernel across a procedurally drawn image and watch the feature map light up — switch between blur, sharpen, Sobel edges, emboss and a Laplacian, or hand-craft your own kernel and chain a second layer.
Hunt the maximum of an expensive, hidden function in as few samples as possible — a Gaussian-process surrogate fits the points so far, draws its mean and a ±2σ uncertainty band, and an acquisition function picks the next, smartest place to look.
Train a real neural autoencoder live: tiny glyphs are squeezed through a 2-D bottleneck and rebuilt by a decoder, the reconstructions sharpening epoch by epoch while a latent map blooms into class clusters — then click anywhere in that latent plane to watch the decoder dream a new glyph into being.
一张 Kohonen 网格，它会褶皱、拉伸并披覆在任意数据分布之上——把拓扑结构的发现可视化呈现。
看一个表格型强化学习智能体从零开始构建导航策略——在陷阱中跌撞、发现奖励，并把最优箭头刻入网格的每一个格子。
看 1958 年的 Rosenblatt 神经元一次次修正，逐步在你的数据中划出一条决策线。
看一个手写的神经网络通过反向传播学习分离二维点云时，实时弯折它的决策边界。
GPT 的祖先——根据前面几个词元预测下一个，并看着连贯性随着阶数增长而逐渐浮现。
看 Lloyd 算法把散点图切割成鲜活的 Voronoi 区域，质心实时追踪并锁定各自的簇。
看一个带噪声或被半擦除的图案如何回弹到已存储的记忆，神经元网络一路下坡滚入吸引子。
五种基于梯度的优化器同时冲下同一片损失地形，揭示为何 Adam 在速度、稳定性和逃离鞍点方面胜过朴素的 SGD。
漫游一个手工构建的语义空间，其中 国王 − 男人 + 女人 恰好落到 王后，意义的簇团绽放出霓虹般的色彩。
看 CART 分类器把你的二维数据切分成轴对齐的区域——每次只做一次信息量最大的分裂。
看 ε-贪心、UCB1 与汤普森采样竞相找出最佳的老虎机臂——算法在探索与利用之间博弈时，累积遗憾被实时绘制出来。
看 Transformer 如何决定关注什么——通过实时热图和二部注意力流，让 softmax(QKᵀ/√d) 变得直观可感。