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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/browser-based-models-tensorflow
课程评论：没有评论
课程名称：基于浏览器的 TensorFlow.js 模型 课程概述：将机器学习模型应用于现实世界的过程远不止建模。这门课程将教你如何在不同的部署场景中导航，更有效地利用数据来训练模型。在这门课程中，你将学习如何使用 TensorFlow.js 在任何浏览器中训练和运行机器学习模型，掌握在浏览器中处理数据的技巧，最后构建一个计算机视觉项目，能够识别和分类来自网络摄像头的物体。 此专业课程建立在我们的 TensorFlow 实践专业课程之上。如果你是 TensorFlow 的新手，建议首先学习 TensorFlow 实践专业课程。为了深入理解神经网络的工作原理，我们建议你学习深度学习专业课程。 课程大纲： 第一部分：TensorFlow.js 入门 描述：欢迎参加基于浏览器的 TensorFlow.js 模型课程，这是 TensorFlow 数据和部署专业课程的第一门课。在此课程中，我们将探讨如何在浏览器中训练机器学习模型，以及如何使用 JavaScript 进行推断。这将使你能够直接在浏览器中以及在像 Node.js 这样的后端服务器上使用机器学习。在课程的第一周，我们将使用 JavaScript 构建一些基本模型，并在简单的网页中执行它们。 第二部分：浏览器中的图像分类 描述：本周我们将关注计算机视觉问题，包括使用 JavaScript 时的一些独特考量，例如处理成千上万的图像进行训练。到本模块结束时，你将能够构建一个网站，允许你在浏览器中绘制并识别你的手写数字！ 第三部分：将模型转换为 JSON 格式 描述：本周我们将学习如何将使用 Python 创建的模型转换为 JSON 格式，以便在浏览器中使用 JavaScript 运行。我们将首先查看两个已经预转换的模型，其中一个是毒性分类器，使用自然语言处理确定一个短语在多个类别中是否具有毒性；另一个是 Mobilenet，可用于检测图像内容。在本模块结束时，你将自己在 Python 中训练一个模型，并使用 tensorflow.js 转换器将其转换为 JSON 格式。 第四部分：使用预训练模型的迁移学习 描述：创建基于浏览器的机器学习应用程序时，你需要了解迁移学习的工作原理。本周你将构建一个完整的网站，使用 TensorFlow.js 捕捉来自网络摄像头的数据，并重新训练 mobilenet 来识别石头、剪刀、布的手势。
Part: 1
Title:Introduction to TensorFlow.js
Description:Welcome to Browser-based Models with TensorFlow.js, the first course of the TensorFlow for Data and Deployment Specialization. In this first course, we’re going to look at how to train machine learning models in the browser and how to use them to perform inference using JavaScript. This will allow you to use machine learning directly in the browser as well as on backend servers like Node.js. In the first week of the course, we are going to build some basic models using JavaScript and we'll execute them in simple web pages.
Part: 2
Title:Image Classification In the Browser
Description:This week we'll look at Computer Vision problems, including some of the unique considerations when using JavaScript, such as handling thousands of images for training. By the end of this module you will know how to build a site that lets you draw in the browser and recognizes your handwritten digits!
Part: 3
Title:Converting Models to JSON Format
Description:This week we'll see how to take models that have been created with TensorFlow in Python and convert them to JSON format so that they can run in the browser using Javascript. We will start by looking at two models that have already been pre-converted. One of them is going to be a toxicity classifier, which uses NLP to determine if a phrase is toxic in a number of categories; the other one is Mobilenet which can be used to detect content in images. By the end of this module, you will train a model in Python yourself and convert it to JSON format using the tensorflow.js converter.
Part: 4
Title:Transfer Learning with Pre-Trained Models
Description:One final work type that you'll need when creating Machine Learned applications in the browser is to understand how transfer learning works. This week you'll build a complete web site that uses TensorFlow.js, capturing data from the web cam, and re-training mobilenet to recognize Rock, Paper and Scissors gestures.
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. In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam. 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.
