DeepLearning.AI
Advanced Computer Vision with TensorFlow
DeepLearning.AI

Advanced Computer Vision with TensorFlow

Laurence Moroney
Eddy Shyu

Instructors: Laurence Moroney

42,181 already enrolled

Gain insight into a topic and learn the fundamentals.
4.8

(509 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 19 hours
Learn at your own pace
92%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.8

(509 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 19 hours
Learn at your own pace
92%
Most learners liked this course

Details to know

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Assessments

4 assignments

Taught in English

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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.
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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 assignment1 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 assignment1 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 assignment1 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 assignment1 programming assignment4 ungraded labs

Instructors

Instructor ratings
4.8 (128 ratings)
Laurence Moroney
DeepLearning.AI
19 Courses530,041 learners

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DeepLearning.AI

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4.8

509 reviews

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