|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/device-based-models-tensorflow
课程评论：没有评论
课程名称：基于设备的模型与TensorFlow Lite 概述：将机器学习模型应用于现实世界不仅仅是建模。这门专业课程将教你如何应对各种部署场景，更有效地利用数据训练模型。第二部分课程将介绍如何在移动应用程序中运行机器学习模型。你将学习如何为低功耗、使用电池的设备准备模型，并在Android和iOS平台上执行这些模型。最后，你将探索如何使用TensorFlow在Raspberry Pi和微控制器上部署嵌入式系统。 本专业课程是基于我们的TensorFlow实践专业课程。如果你是TensorFlow新手，建议先参加TensorFlow实践专业课程。为了深入理解神经网络的运作，建议学习深度学习专业课程。 课程大纲： 1. **基于设备的模型与TensorFlow Lite** - 介绍TensorFlow Lite，这是一个令人兴奋的技术，可以将模型直接应用于移动设备。你将深入了解该技术及其工作原理，学习如何优化模型以适应移动使用的需求，处理电池和处理能力的限制。同时，学习如何在Android和iOS上构建运行这些模型的应用程序，并了解如何在这些环境中使用TensorFlow Lite解释器，最后探索在Raspberry Pi和SparkFun Edge板上运行模型的嵌入式系统。 2. **在Android应用中运行TF模型** - 本周你学习了TensorFlow Lite，以及如何将你的模型从TensorFlow转换为TensorFlow Lite格式。同时学习了独立的TensorFlow Lite解释器，用于测试这些模型，并通过练习将Fashion MNIST基础的模型转换为TensorFlow Lite并使用解释器进行测试。 3. **在iOS上构建TensorFlow模型** - 这周将进行与上周类似的任务，学习如何在iOS上运行模型。需要具备一定的Swift编程基础以完全理解课程内容，但即使没有这方面的经验，课程内容也会很有趣，并将帮助你构建在这一重要操作系统上运行的各种机器学习应用程序。 4. **设备上的TensorFlow Lite** - 经过对TensorFlow Lite的学习，以及在Android和iOS上构建应用程序，你将探讨如何在Raspberry Pi等嵌入式系统上运行模型。Raspberry Pi是一个完整的Linux系统，可以运行Python，允许你使用完整的TensorFlow进行训练和推理，或者仅使用解释器进行推理。建议使用后者，因为在Pi上训练可能会比较缓慢！
Part: 1
Title:Device-based models with TensorFlow Lite
Description:Welcome to this course on TensorFlow Lite, an exciting technology that allows you to put your models directly and literally into people's hands. You'll start with a deep dive into the technology, and how it works, learning about how you can optimize your models for mobile use -- where battery power and processing power become an important factor. You'll then look at building applications on Android and iOS that use models, and you'll see how to use the TensorFlow Lite Interpreter in these environments. You'll wrap up the course with a look at embedded systems and microcontrollers, running your models on Raspberry Pi and SparkFun Edge boards.
Part: 2
Title:Running a TF model in an Android App
Description:Last week you learned about TensorFlow Lite and you saw how to convert your models from TensorFlow to TensorFlow Lite format. You also learned about the standalone TensorFlow Lite Interpreter which could be used to test these models. You wrapped with an exercise that converted a Fashion MNIST based model to TensorFlow Lite and then tested it with the interpreter.
Part: 3
Title:Building the TensorFLow model on IOS
Description:The other popular mobile operating system is, of course, iOS. So this week you'll do very similar tasks to last week -- learning how to take models and run them on iOS. You'll need some programming background with Swift for iOS to fully understand everything we go through, but even if you don't have this expertise, I think this weeks content is something you'll find fun to explore -- and you'll learn how to build a variety of ML applications that run on this important operating system!
Part: 4
Title:TensorFlow Lite on devices
Description:Now that you've looked at TensorFlow Lite and explored building apps on Android and iOS that use it, the next and final step is to explore embedded systems like Raspberry Pi, and learn how to get your models running on that. The nice thing is that the Pi is a full Linux system, so it can run Python, allowing you to either use the full TensorFlow for Training and Inference, or just the Interpreter for Inference. I'd recommend the latter, as training on a Pi can be slow!
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.
