September 2020
Intermediate to advanced
434 pages
9h 6m
Chinese
Content preview from TinyML：基于TensorFlow Lite 在Arduino 和超低功耗微控制器上部署机器学习
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的优势，还是可以适当地放宽对电池寿命、成本和尺寸的要求。与在嵌入式世界中进行
开发相比，在具有完整现代操作系统（如 Linux）的平台上编程要容易得多，也快得多。
你可以以不到 25 美元的价格获得完整的桌面系统（如树莓派（Raspberry Pi））以及许多
外围设备（如摄像头和各种传感器）。如果你需要运行计算繁重的神经网络，NVIDIA 的
Jetson 系列主板起价为 99 美元，并以较小的尺寸提供强大的软件堆栈。这些设备的最大
缺点是，它们会消耗几瓦的能源，如果使用电池供电，那么其寿命最多只有几小时或者
几天，具体取决于储能设备的物理大小。只要延迟没有硬性限制，你甚至可以利用许多
强大的云服务器来运行神经网络的运算，让客户端设备来处理接口和网络通信。
我们坚信能够在任何地方部署嵌入式机器学习系统，但是如果你只是想要确认某个想法
是否完全可行，我们强烈建议你先尝试使用一个更简单、更易于试验的设备进行原型
（prototype）开发。在嵌入式系统上开发是一件非常麻烦的事情，因此，在深入开发之
前，越早弄清楚应用程序的实际需求，成功的概率也就越大。
举一个实际的例子，假设你想构建一个设备来监视绵羊的健康状况，最终产品将需要在
没有良好网络连接的环境中运行几个星期甚至几个月，因此它必须是嵌入式系统。但
是，开始的时候你也许并不想使用这样棘手的编程设备，因为你还不知道很多重要的细
节，例如要运行什么样的模型、需要什么传感器以及需要什么操作来收集数据。你也还
没有任何可以用于训练的数据。为了引导你的工作 ...
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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.