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Advanced Computer Vision with TensorFlow
  • Level Professional
  • المدة 19 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images. c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more. d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

الوحدات

Concepts in Computer Vision

    • 3 Videos
    • 2 Readings
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3 Videos

  • Welcome to Course 3
  • Classification and Object Detection Intro
  • Segmentation Intro

2 Readings

  • Prerequisite & References
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Transfer Learning

    • 3 Videos
    • 1 Lab
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1 Labs

  • Transfer Learning

3 Videos

  • Why Transfer Learning?
  • What is Transfer Learning?
  • Options in Transfer Learning

Advanced Transfer Learning

    • 2 Videos
    • 1 Lab
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1 Labs

  • Transfer Learning with ResNet 50

2 Videos

  • Transfer Learning with ResNet50
  • ResNet50 in code

Object Localization and Detection

    • 2 Videos
    • 1 Lab
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1 Labs

  • Image Classification and Object Localization

2 Videos

  • Network architecture for Object Localization
  • Evaluating Object Localization

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 1

Week 1 Quiz: Introduction and Concepts of Computer Vision

    • 1 Assignment
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1 Assignment

  • Introduction and Concepts of Computer Vision

Assignment: Bird Boxes

    • 1 Programming
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1 Programming

  • Bird Boxes

Object Detection

    • 4 Videos
    • 2 Readings
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4 Videos

  • Object Detection and Sliding Windows
  • R-CNN
  • Fast R-CNN
  • Faster R-CNN

2 Readings

  • References: Amazon Rekognition, PowerAI & DIGITS
  • Reference: R-CNN, Fast R-CNN

Object Detection in TensorFlow

    • 2 Videos
    • 1 Readings
    • 2 Lab
Show info about module content

2 Labs

  • Implement Simple Object Detection
  • Predicting Bounding Boxes for Object Detection

2 Videos

  • Getting the Model from TensorFlow Hub
  • Running the Model on an Image

1 Readings

  • Reference: TensorFlow Hub

Object Detection APIs

    • 2 Videos
    • 2 Readings
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2 Videos

  • Installation and overview of APIs
  • Visualization with APIs

2 Readings

  • Read about the Object Detection API
  • Use the Object Detection API

Retraining with the Object Detection API

    • 4 Videos
    • 2 Readings
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4 Videos

  • Loading a RetinaNet Model
  • Loading Weights
  • Data Prep and Training Overview
  • Custom Training Loop Code

2 Readings

  • Reference: RetinaNet, Model Garden
  • Eager Few Shot Object Detection

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 2

Week 2 Quiz: Object Detection

    • 1 Assignment
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1 Assignment

  • Object Detection

Assignment: Zombie Detector

    • 1 Programming
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1 Programming

  • Zombie Detector

Image Segmentation Overview

    • 7 Videos
    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Implement a Fully Convolutional Neural Network

7 Videos

  • Image Segmentation Overview
  • Popular Image Segmentation Architectures
  • FCN Architecture Details
  • Upsampling Methods
  • Encoder in Code
  • Decoder in Code
  • Evaluation with IoU and Dice Score

2 Readings

  • References: FCN
  • Reference: CamVid

U-Net

    • 3 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Implement a UNet

3 Videos

  • U-Net Overview
  • U-Net Code: Encoder
  • U-Net Code: Decoder

1 Readings

  • Reference: U-Net

Instance Segmentation

    • 1 Videos
    • 1 Lab
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1 Labs

  • Instance Segmentation Demo

1 Videos

  • Instance Segmentation

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 3

Week 3 Quiz: Image Segmentation

    • 1 Assignment
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1 Assignment

  • Image Segmentation

Assignment: Image Segmentation of Handwritten Digits

    • 1 Programming
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1 Programming

  • Image Segmentation of Handwritten Digits

Intro to Visualization and Interpretation

    • 3 Videos
    • 2 Lab
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2 Labs

  • Class Activation Maps with Fashion MNIST (Lab #1)
  • Class Activation Maps "Cats vs Dogs" (Lab #2)

3 Videos

  • Why Interpretation Matters?
  • Class Activation Maps
  • Fashion MNIST Class Activation Map code walkthrough

Saliency

    • 1 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Saliency Maps (Lab #3)

1 Videos

  • Saliency

Gradients and Class Activation Maps

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • GradCAM (Lab #4)

1 Videos

  • GradCAM

1 Readings

  • Reference: GradCam

Improving a model with Interpretation

    • 1 Videos
    • 1 Readings
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1 Videos

  • ZFNet

1 Readings

  • Reference: ZFNet

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 4

Week 4 Quiz: Visualization and Interpretation

    • 1 Assignment
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1 Assignment

  • Visualization and Interpretation

End of Access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Assignment: Cats vs Dogs Saliency Maps

    • 1 Programming
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1 Programming

  • Cats vs Dogs Saliency Maps

Course Resources

    • 1 Readings
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1 Readings

  • References

Acknowledgments

    • 1 Readings
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1 Readings

  • Acknowledgments

Auto Summary

Explore advanced computer vision techniques with TensorFlow in this professional course by Coursera. Learn image classification, segmentation, object localization, and detection using models like regional-CNN, ResNet-50, and Mask-RCNN. Designed for early and mid-career software and ML engineers, this 1140-minute course delves into transfer learning, model customization, and ML interpretation methods. Expand your expertise with hands-on projects and advanced TensorFlow features. Available with a Starter subscription.

Laurence Moroney
Instructor

Laurence Moroney