TensorFlow: Data and Deployment Specialization | Coursera

What you’ll learn
• Run models in your browser using TensorFlow.js
• Prepare and deploy models on mobile devices using TensorFlow Lite
• Access, organize, and process training data more easily using TensorFlow Data Services
• Explore four advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard

• Tensorflow
• Object Detection
• Machine Learning
• JavaScript
• Advanced deployment

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.

1. Browser-based Models with TensorFlow.js
2. Device-based Models with TensorFlow Lite
3. Data Pipelines with TensorFlow Data Services
4. Advanced Deployment Scenarios with TensorFlow

In the TensorFlow: Data and Deployment Specialization, you will learn to apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more, implementing projects you can add to your portfolio and show in interviews.

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