TensorFlow: Data and Deployment Specialization from deeplearning.ai

We have already looked at TOP 100 Coursera Specializations and today we will check out TensorFlow: Data and Deployment Specialization from deeplearning.ai.

Coursera Specialization is a series of courses that help you master a skill. To begin, you can enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. You can either complete just one course or you can pause your learning or end your subscription at any time.

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, use APIs to control how data splitting, and process all types of unstructured data. 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 AI. 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 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.

What will you learn

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

There are 4 Courses in this Specialization

#1.Browser-based Models with TensorFlow.js

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 in the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

What will you learn

• Train and run inference in a browser
• Handle data in a browser
• Build an object classification and recognition model using a webcam

#2.Device-based Models with TensorFlow Lite

This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers.

What will you learn

• Prepare models for battery-operated devices
• Deploy models on embedded systems like Raspberry Pi and microcontrollers
• Execute models on Android and iOS platforms

#3.Data Pipelines with TensorFlow Data Services

In this third course, you’ll use a suite of tools in TensorFlow to more effectively leverage data and train your model. You’ll learn how to leverage built-in datasets with just a few lines of code, use APIs to control how you split your data, and process all types of unstructured data.

What will you learn

• Leverage built-in datasets with just a few lines of code
• Process all types of unstructured data
• Use APIs to control how you split your data

#4.Advanced Deployment Scenarios with TensorFlow

In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy.

What will you learn

• Use TensorFlow Serving to do inference over the web
• Evaluate how your models work and share model metadata using TensorBoard
• Navigate TensorFlow Hub, a repository of models that you can use for transfer learning
• Explore federated learning and how to retrain deployed models while maintaining data privacy

Useful Resources :

• Trending Skill: Deep Learning Course Collection
• ULTIMATE GUIDE to Coursera Specializations That Will Make Your Career Better (Over 100+ Specializations covered)
• Get Job Ready with Professional Certificates from Coursera

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