Word2vec

打「貓」「小貓」「喵星人」，AI 怎麼知道講的是同一種東西？答案是 embedding：把文字變成座標，再用夾角量意思相不相近。這篇拆開它怎麼運作，順便聊聊「king − man + woman = queen」那個經典其實灌了點水。

Search for 'make my laptop quieter' and get a page about 'reducing fan noise' with zero words in common. That's embeddings: text turned into coordinates, with meaning measured as the angle between two points. Here's how the trick works, and where it's oversold.

TensorFlow 練習：用 skip-gram 實作 word2vec 詞向量，並拆開 nce_loss 那一行：訓練時為什麼不掃整張詞彙表，改抽 K 個負面樣本問是非題，以及那些負面樣本是怎麼抽出來的。