DeepLearning.AI
Advanced Computer Vision with TensorFlow
DeepLearning.AI

Advanced Computer Vision with TensorFlow

This course is part of TensorFlow: Advanced Techniques Specialization

Taught in English

Some content may not be translated

Laurence Moroney
Eddy Shyu

Instructors: Laurence Moroney

37,617 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.8

(481 reviews)

|

92%

Intermediate level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(481 reviews)

|

92%

Intermediate level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the TensorFlow: Advanced Techniques Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

Get a conceptual overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models.

What's included

10 videos3 readings1 quiz1 programming assignment3 ungraded labs

This week, you’ll get an overview of some popular object detection models, such as regional-CNN and ResNet-50. You’ll use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, and also build your own models for object detection. By using transfer learning, you will train a model to detect and localize rubber duckies using just five training examples. You’ll also get to manually label your own rubber ducky images!

What's included

12 videos8 readings1 quiz1 programming assignment2 ungraded labs

This week is all about image segmentation using variations of the fully convolutional neural network. With these networks, you can assign class labels to each pixel, and perform much more detailed identification of objects compared to bounding boxes. You’ll build the fully convolutional neural network, U-Net, and Mask R-CNN this week to identify and detect numbers, pets, and even zombies!

What's included

11 videos4 readings1 quiz1 programming assignment3 ungraded labs

This week, you’ll learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions. You’ll also implement class activation maps, saliency maps, and gradient-weighted class activation maps to identify which parts of an image are being used by your model to make its predictions. You’ll also see an example of how visualizing a model’s intermediate layer activations can help to improve the design of a famous network, AlexNet.

What's included

6 videos6 readings1 quiz1 programming assignment4 ungraded labs

Instructors

Instructor ratings
4.8 (124 ratings)
Laurence Moroney
DeepLearning.AI
15 Courses495,982 learners

Offered by

DeepLearning.AI

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