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Laurence Moroney

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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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.

Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate.

Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

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What's inside

Four courses

Browser-based Models with TensorFlow.js

(0 hours)
Bringing a machine learning model into the real world involves more than just modeling. This course will teach you how to navigate various deployment scenarios and use data more effectively to train your model. 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.

Device-based Models with TensorFlow Lite

(0 hours)
Bringing a machine learning model into the real world involves more than just modeling. This course teaches you how to navigate deployment scenarios and use data effectively to train your model.

Data Pipelines with TensorFlow Data Services

(0 hours)
Bringing a machine learning model into the real world involves a lot more than just modeling. This course will teach you how to navigate various deployment scenarios and use data more effectively to train your model.

Advanced Deployment Scenarios with TensorFlow

(0 hours)
Bringing a machine learning model into the real world involves more than just modeling. This course will teach you how to navigate various deployment scenarios and use data more effectively to train your model.

Learning objectives

  • 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

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