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第 1 門課程(共 4 門)
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中級

Basic understanding of JavaScript

完成時間大約為18 小時
英語(English)
字幕:英語(English)

您將學到的內容有

  • Train and run inference in a browser

  • Handle data in a browser

  • Build an object classification and recognition model using a webcam

您將獲得的技能

Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 1 門課程(共 4 門)
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根據您的日程表重置截止日期。
中級

Basic understanding of JavaScript

完成時間大約為18 小時
英語(English)
字幕:英語(English)

講師

提供方

deeplearning.ai 徽標

deeplearning.ai

教學大綱 - 您將從這門課程中學到什麼

內容評分Thumbs Up95%(1,043 個評分)Info
1

1

完成時間為 5 小時

Introduction to TensorFlow.js

完成時間為 5 小時
11 個視頻 (總計 30 分鐘), 7 個閱讀材料, 3 個測驗
11 個視頻
Course Introduction, A Conversation with Andrew Ng1分鐘
A Few Words From Laurence2分鐘
Building the Model3分鐘
Training the Model3分鐘
First Example In Code4分鐘
The Iris Dataset1分鐘
Reading the Data4分鐘
One-hot Encoding1分鐘
Designing the NN2分鐘
Iris Classifier In Code6分鐘
7 個閱讀材料
Getting Your System Ready10分鐘
Downloading the Coding Examples and Exercises10分鐘
Your First Model10分鐘
Iris Dataset Documentation10分鐘
Using the Web Server10分鐘
Iris Classifier10分鐘
Week 1 Wrap up10分鐘
2 個練習
Quiz 1
One-Hot Encoding
2

2

完成時間為 4 小時

Image Classification In the Browser

完成時間為 4 小時
8 個視頻 (總計 27 分鐘), 5 個閱讀材料, 2 個測驗
8 個視頻
Creating a Convolutional Net with JavaScript4分鐘
Visualizing the Training Process2分鐘
What Is a Sprite Sheet?1分鐘
Using the Sprite Sheet2分鐘
Using tf.tidy() to Save Memory1分鐘
A Few Words From Laurence24
MNIST Classifier In Code13分鐘
5 個閱讀材料
tjs-vis Documentation10分鐘
MNIST Sprite Sheet10分鐘
MNIST Classifier10分鐘
Week 2 Wrap up10分鐘
Exercise Description10分鐘
1 個練習
Week 2 Quiz
3

3

完成時間為 5 小時

Converting Models to JSON Format

完成時間為 5 小時
12 個視頻 (總計 28 分鐘), 7 個閱讀材料, 2 個測驗
12 個視頻
A Few Words From Laurence1分鐘
Pre-trained TensorFlow.js Models49
Toxicity Classifier3分鐘
Toxicity Classifier In Code3分鐘
MobileNet49
Using MobileNet1分鐘
Training Results1分鐘
MobileNet Example In Code3分鐘
Converting Models to JavaScript4分鐘
Converting Models to JavaScript In Code2分鐘
Linear Example In Code1分鐘
7 個閱讀材料
Important Links10分鐘
Toxicity Classifier10分鐘
Classes Supported by MobileNet10分鐘
Image Classification Using MobileNet10分鐘
Linear Model10分鐘
Week 3 Wrap up10分鐘
Optional - Install Wget (Only If Needed)10分鐘
1 個練習
Week 3 Quiz
4

4

完成時間為 4 小時

Transfer Learning with Pre-Trained Models

完成時間為 4 小時
11 個視頻 (總計 26 分鐘), 3 個閱讀材料, 2 個測驗
11 個視頻
A Few Words From Laurence53
Building a Simple Web Page2分鐘
Retraining the MobileNet Model1分鐘
The Training Function2分鐘
Capturing the Data3分鐘
The Dataset Class2分鐘
Training the Network with the Captured Data1分鐘
Performing Inference4分鐘
Rock Paper Scissors In Code4分鐘
A Conversation with Andrew Ng1分鐘
3 個閱讀材料
Rock Paper Scissors10分鐘
Exercise Description10分鐘
Wrap up10分鐘
1 個練習
Week 4 Quiz

審閱

來自BROWSER-BASED MODELS WITH TENSORFLOW.JS的熱門評論

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關於 TensorFlow: Data and Deployment 專項課程

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. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment....
TensorFlow: Data and Deployment

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