[null,null,["最后更新时间 (UTC)：2024-08-13。"],[[["This module explains how to create embeddings, which are lower-dimensional representations of sparse data that address the problems of large input vectors and lack of meaningful relations between vectors in one-hot encoding."],["One-hot encoding creates large input vectors, leading to a huge number of weights in a neural network, requiring more data, computation, and memory."],["One-hot encoding vectors lack meaningful relationships, failing to capture semantic similarities between items, like the example of hot dogs and shawarmas being more similar than hot dogs and salads."],["Embeddings offer a solution by providing dense vector representations that capture semantic relationships and reduce the dimensionality of data, improving efficiency and performance in machine learning models."],["This module assumes familiarity with introductory machine learning concepts like linear regression, categorical data, and neural networks."]]],[]]
![图 2. 上图：罗宋汤的独热编码的可视化。
矢量 [1, 0, 0, 0, ..., 0] 显示在六个方框上方，
每个都与左对齐
与其中一个矢量数对应。方框（从左到右）
包含以下图片：罗宋汤、热狗、沙拉、披萨、[空]、
沙威玛。中间图：热狗的独热编码的可视化。
矢量 [0, 1, 0, 0, ..., 0] 显示在六个方框的上方，每个
从左到右对齐。这些盒子有
从左到右的图片与罗宋图上的图片相同
。下图：沙威玛的独热编码的可视化效果。通过
矢量 [0, 0, 0, 0, ..., 1] 显示在六个方框上方，每个方框对齐
从左到右绘制一个矢量数字。这些盒子有
从左到右的图片与罗宋汤和热狗的图片相同
和可视化。](https://developers.google.cn/static/machine-learning/crash-course/embeddings/images/food_images_one_hot_encodings.png?authuser=19&hl=lt)