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Image Understanding with TensorFlow on GCP

Google Cloud and Google via Coursera

0 Reviews 41 students interested
  • Provider Coursera
  • Subject Deep Learning
  • Cost $50
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Effort 5-7 hours a week
  • Start Date
  • Duration 2 weeks long
  • Learn more about MOOCs

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Overview

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***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***

This is the third course of the Advanced Machine Learning on GCP specialization. In this course,
We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models.

You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Advanced Machine Learning with TensorFlow on GCP’ specialization.

Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).

You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.

Your challenge awaits – begin learning on Coursera today!

Syllabus

WEEK 1

Welcome to Image Understanding with TensorFlow on GCP

In this introductory module you will learn about the rapid growth in high-resolution image data available and the types of applications that it can be applied to. We’ll also cover image data as inputs to your model.

 

Linear and DNN Models

We’ll start with a brief introduction where we’ll cover the image dataset you will be using for part of this course. Then we’ll tackle an image classification problem with a linear model in TensorFlow. After that we’ll move onto tackling the same problem using a Deep Neural Network. Lastly, we’ll close with a discussion and application of dropout which is a regularization technique for neural networks to help prevent them from memorizing our training dataset.

 

Convolutional Neural Networks (CNNs)

This module will introduce Convolutional Neural Networks or CNNs for short, and get you started with implementing CNNs using TensorFlow. Since 2012, CNN based systems achieved unparalleled performance on tasks like image recognition and even at playing the ancient board game of Go against the top human champions.

 

WEEK 2

Dealing with Data Scarcity

In this module, we’ll focus on data scarcity, what it is, why it’s important, and, before moving onto building ML models, what you need to do about it

 

Going Deeper Faster

In this module, you will learn how to train deeper, more accurate networks and do such training faster.You will learn about common problems that arise when training deeper networks, and how researchers have been able to address these issues.

 

Pre-built ML Models for Image Classification

Welcome to the last module of the Image Classification course. Now that you have build your own image classifiers using linear, DNN, and CNN models with TensorFlow, it’s time to experiment with pre-built image models. In most cases, you will want to try these before investing your time in developing custom TensorFlow code for a model

 

Summary

In this final module, we will review the core concepts covered in this image classification course. You will recall creating classifiers with linear models, DNNs, DNNs with Dropout, Convolutional Neural Networks (CNNs), and lastly with pre-built models like the Cloud Vision API and AutoML Vision.

 

Taught by

Google Cloud Training

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