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.
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TensorFlow: Data and Deployment 专项课程deeplearning.ai课程信息
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第 1 门课程（共 4 门）
中级
Basic understanding of JavaScript
完成时间大约为22 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
您将获得的技能
- Convolutional Neural Network
- Machine Learning
- Tensorflow
- Object Detection
- TensorFlow.js
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
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100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 4 门）
中级
Basic understanding of JavaScript
完成时间大约为22 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
完成时间为 7 小时
Introduction to TensorFlow.js
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.
完成时间为 7 小时
11 个视频 （总计 30 分钟）, 7 个阅读材料, 5 个测验
完成时间为 5 小时
Image Classification In the Browser
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!
完成时间为 5 小时
8 个视频 （总计 27 分钟）, 5 个阅读材料, 3 个测验
完成时间为 5 小时
Converting Models to JSON Format
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.
完成时间为 5 小时
12 个视频 （总计 28 分钟）, 6 个阅读材料, 3 个测验
完成时间为 5 小时
Transfer Learning with Pre-Trained Models
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.
完成时间为 5 小时
11 个视频 （总计 26 分钟）, 3 个阅读材料, 3 个测验
审阅
- 5 stars81.49%
- 4 stars14.26%
- 3 stars2.67%
- 2 stars0.66%
- 1 star0.89%
来自BROWSER-BASED MODELS WITH TENSORFLOW.JS的热门评论
由 JC 提供Mar 2, 2020
I really enjoy working on the programming assignments of this course especially the Week 4 one which is fun and have a lot to learn!
由 NV 提供Apr 13, 2020
The course is extremely fun to learn. Lot of concepts are covered through a practical approach. The teaching of the instructor is awesome.
由 JG 提供Dec 19, 2020
Excellent course!!! It is actually a milestone for people like me who have trained models in Jupyter notebooks, but Tensorflow JS is actually a great way for the models to become 'alive'! Thanks!
由 EJ 提供Mar 17, 2021
This course is very practical and interesting. I enjoyed the excitement I got along the way.\n\nIt was modeled to make you pass as long as you want to pass.\n\nThank you Laurence and Andrew.
关于 TensorFlow: Data and Deployment 专项课程
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.
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