Image Understanding with TensorFlow on GCP
- Offered byCoursera
Image Understanding with TensorFlow on GCP at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Image Understanding with TensorFlow on GCP at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the Advanced Machine Learning on Google Cloud Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 12 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Image Understanding with TensorFlow on GCP at Coursera Course 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
Image Understanding with TensorFlow on GCP at Coursera Curriculum
Welcome to Image Understanding with TensorFlow on GCP
Course Introduction
Getting Started with Google Cloud Platform and Qwiklabs
Images as Visual Data
Structured vs Unstructured Data
How to Send Feedback
Images as Visual Data
Introduction
Linear Models
Lab Intro: Linear Models for Image Classification
Lab Solution: Linear Models for Image Classification
DNN Models Review
Lab Intro: DNN Models for Image Classification
Lab Solution: DNN Models for Image Classification
Review: What is Dropout?
Lab Intro: DNNs with Dropout Layer for Image Classification
Lab Solution: DNNs with Dropout Layer for Image Classification
Linear and DNN Models
Introduction
Understanding Convolutions
CNN Model Parameters
Working with Pooling Layers
Implementing CNNs with TensorFlow
Lab Intro: Creating an Image Classifier with a Convolutional Neural Network
Lab Solution: Creating an Image Classifier with a Convolutional Neural Network
CNNs
Dealing with Data Scarcity
The Data Scarcity Problem
Data Augmentation
Lab Intro: Implementing image augmentation
Lab Solution: Implementing image augmentation
Transfer Learning
Lab Intro: Implementing Transfer Learning
Lab Solution: Implementing Transfer Learning
No Data, No Problem
Dealing with Data Scarcity
Introduction
Batch Normalization
Residual Networks
Accelerators (CPU vs GPU, TPU)
TPU Estimator
Demo: TPU Estimator
Neural Architecture Search
Summary
Going Deeper, Faster
Introduction
Pre-built ML Models
Cloud Vision API
Demo: Vision API
AutoML Vision
Demo: AutoML
AutoML Architecture
Lab Intro: Training with Pre-built ML Models using Cloud Vision API and AutoML
Lab Solution: Training with Pre-built ML Models using Cloud Vision API and AutoML
Pre-built Models
Summary
Additional Resources
Image Understanding with TensorFlow on GCP at Coursera Admission Process
Important Dates
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