Device-based Models with TensorFlow Lite

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课程主页: https://www.coursera.org/archive/device-based-models-tensorflow

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Device-based models with TensorFlow Lite
Running a TF model in an Android App
Building the TensorFLow model on IOS
TensorFlow Lite on devices

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Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

TensorFlow Lite的基于设备的模型:将机器学习模型带入现实世界不仅涉及建模,还涉及很多其他内容。本专业知识将教您如何导航各种部署方案并更有效地使用数据来训练模型。 第二门课程教您如何在移动应用程序中运行机器学习模型。您将学习如何为功率较低的电池供电设备准备模型,然后在Android和iOS平台上执行模型。最后,您将探索如何在Raspberry Pi和微控制器上使用TensorFlow在嵌入式系统上进行部署。 该专业化基于我们的TensorFlow实践专业化。如果您不熟悉TensorFlow,我们建议您首先参加TensorFlow实践专业化课程。为了对神经网络的工作方式有更深入的基础了解,我们建议您参加“深度学习专业化”课程。

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