DeepLearning.AI - Browser-based Models with TensorFlow.js
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Browser-based Models with TensorFlow.js at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Browser-based Models with TensorFlow.js at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 4 in the TensorFlow: Data and Deployment Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Basic understanding of JavaScript
- Approx. 18 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Browser-based Models with TensorFlow.js at Coursera Course details
- 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.
Browser-based Models with TensorFlow.js at Coursera Curriculum
Introduction to TensorFlow.js
Specialization Introduction, A Conversation with Andrew Ng
Course Introduction, A Conversation with Andrew Ng
A Few Words From Laurence
Building the Model
Training the Model
First Example In Code
The Iris Dataset
Reading the Data
One-hot Encoding
Designing the NN
Iris Classifier In Code
Getting Your System Ready
Downloading the Coding Examples and Exercises
Your First Model
Iris Dataset Documentation
Using the Web Server
Iris Classifier
Week 1 Wrap up
Quiz 1
One-Hot Encoding
Image Classification In the Browser
Introduction, A Conversation with Andrew Ng
Creating a Convolutional Net with JavaScript
Visualizing the Training Process
What Is a Sprite Sheet?
Using the Sprite Sheet
Using tf.tidy() to Save Memory
A Few Words From Laurence
MNIST Classifier In Code
tjs-vis Documentation
MNIST Sprite Sheet
MNIST Classifier
Week 2 Wrap up
Exercise Description
Week 2 Quiz
Converting Models to JSON Format
Introduction, A Conversation with Andrew Ng
A Few Words From Laurence
Pre-trained TensorFlow.js Models
Toxicity Classifier
Toxicity Classifier In Code
MobileNet
Using MobileNet
Training Results
MobileNet Example In Code
Converting Models to JavaScript
Converting Models to JavaScript In Code
Linear Example In Code
Important Links
Toxicity Classifier
Classes Supported by MobileNet
Image Classification Using MobileNet
Linear Model
Week 3 Wrap up
Optional - Install Wget (Only If Needed)
Week 3 Quiz
Transfer Learning with Pre-Trained Models
Introduction, A Conversation with Andrew Ng
A Few Words From Laurence
Building a Simple Web Page
Retraining the MobileNet Model
The Training Function
Capturing the Data
The Dataset Class
Training the Network with the Captured Data
Performing Inference
Rock Paper Scissors In Code
A Conversation with Andrew Ng
Rock Paper Scissors
Exercise Description
Wrap up
Week 4 Quiz
Browser-based Models with TensorFlow.js at Coursera Admission Process
Important Dates
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