October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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第 13 章
使用 TensorFlow 加载和预处理数据
到目前为止，我们仅使用了适合放入内存的数据集，但是深度学习系统经常在非常大
的数据集上训练，而这些数据集不能完全放入 RAM。读取大型数据集并对其进行有
效预处理可能对其他深度学习库来说很难实现，但是 TensorFlow 借助
Data API
很容
易实现：只需创建一个数据集对象，并告诉它从何处获取数据以及如何对其进行转换。
TensorFlow 负责所有细节的实现，例如多线程、队列、批处理和预取。此外，Data API
与 tf.keras 无缝协同工作！
现成的 Data API 可以读取文本文件（例如 CSV 文件）、具有固定大小记录的二进制
文件以及使用 TensorFlow 的 TFRecord 格式（支持各种大小的记录）的二进制文件。
TFRecord 是一种包含协议缓冲区的灵活高效的二进制格式（一种开源二进制格式）。
Data API 还支持从 SQL 数据库中读取。而且许多开源扩展都可以从各种数据源中读取，
例如 Google 的 BigQuery 服务。
有效读取大数据集并不是唯一的难点：数据也需要进行预处理，通常是归一化的。而且，
它并不总是严格地由数字字段组成，可能存在文本特征、分类特征等。这些需要进行编
码、例如使用独热编码、词袋编码或嵌入（如我们将要看到的，嵌入是一种可训练的密
集向量，表示类别或令牌）。处理所有这些预处理的一种方法是编写自己的自定义预处
理层，也可以使用 Keras 提供的标准预处理层。
在本章中，我们将介绍 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.