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専門講座deeplearning.aiこのコースについて
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中級レベル
Basic understanding of JavaScript
約22時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, ドイツ語, ロシア語, 英語, スペイン語
学習内容
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
習得するスキル
- Convolutional Neural Network
- Machine Learning
- Tensorflow
- Object Detection
- TensorFlow.js
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の1コース
中級レベル
Basic understanding of JavaScript
約22時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, ドイツ語, ロシア語, 英語, スペイン語
提供：
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
シラバス - 本コースの学習内容
7時間で修了
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.
7時間で修了
11件のビデオ （合計30分）, 7 学習用教材, 5 個のテスト
5時間で修了
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!
5時間で修了
8件のビデオ （合計27分）, 5 学習用教材, 3 個のテスト
5時間で修了
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.
5時間で修了
12件のビデオ （合計28分）, 6 学習用教材, 3 個のテスト
5時間で修了
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.
5時間で修了
11件のビデオ （合計26分）, 3 学習用教材, 3 個のテスト
レビュー
- 5 stars81.39%
- 4 stars14.34%
- 3 stars2.69%
- 2 stars0.67%
- 1 star0.89%
BROWSER-BASED MODELS WITH TENSORFLOW.JS からの人気レビュー
MSにより2020年6月29日
it was good, but it heavily depended on knowing html, but it will help with the basics when someone is creating a model for web page or smt
SRにより2021年5月3日
This course has given me a lot of real world exercises. The lessons are concise yet really helpful to start your web-based AI project.
JGにより2020年12月19日
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!
EJにより2021年3月17日
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.
TensorFlow: Data and Deployment専門講座について
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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