About this Course

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Intermediate Level
  • Basic calculus, linear algebra, stats
  • Knowledge of AI, deep learning
  • Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
Approx. 24 hours to complete
English

Skills you will gain

SalienceImage SegmentationModel InterpretabilityClass Activation MapsTensorFlow Object Detection API
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
  • Basic calculus, linear algebra, stats
  • Knowledge of AI, deep learning
  • Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
Approx. 24 hours to complete
English

Offered by

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DeepLearning.AI

Syllabus - What you will learn from this course

Week
1

Week 1

6 hours to complete

Introduction to Computer Vision

6 hours to complete
10 videos (Total 41 min), 2 readings, 2 quizzes
10 videos
Classification and Object Detection Intro4m
Segmentation Intro2m
Why Transfer Learning?4m
What is Transfer Learning?4m
Options in Transfer Learning3m
Transfer Learning with ResNet503m
ResNet50 in code4m
Network architecture for Object Localization3m
Evaluating Object Localization2m
2 readings
Pre-Requisite & References10m
Connect with your mentors and fellow learners on Slack!10m
1 practice exercise
Introduction and Concepts of Computer Vision30m
Week
2

Week 2

6 hours to complete

Object Detection

6 hours to complete
12 videos (Total 45 min), 7 readings, 2 quizzes
12 videos
R-CNN3m
Fast R-CNN3m
Faster R-CNN1m
Getting the Model from TensorFlow Hub1m
Running the Model on an Image2m
Installation and overview of APIs4m
Visualization with APIs3m
Loading a RetinaNet Model5m
Loading Weights4m
Data Prep and Training Overview3m
Custom Training Loop Code4m
7 readings
References: Amazon Rekognition, PowerAI & DIGITS10m
Reference: R-CNN, Fast R-CNN 10m
Reference: TensorFlow Hub10m
Read about the Object Detection API10m
Use the Object Detection API30m
Reference: RetinaNet, Model Garden10m
Eager Few Shot Object Detection30m
1 practice exercise
Object Detection30m
Week
3

Week 3

6 hours to complete

Image Segmentation

6 hours to complete
11 videos (Total 45 min), 3 readings, 2 quizzes
11 videos
Popular Image Segmentation Architectures4m
FCN Architecture Details5m
Upsampling Methods3m
Encoder in Code2m
Decoder in Code4m
Evaluation with IoU and Dice Score4m
U-Net Overview5m
U-Net Code: Encoder3m
U-Net Code: Decoder3m
Instance Segmentation2m
3 readings
References: FCN10m
Reference: CamVid 10m
Reference: U-Net10m
1 practice exercise
Image Segmentation30m
Week
4

Week 4

7 hours to complete

Visualization and Interpretability

7 hours to complete
6 videos (Total 30 min), 4 readings, 2 quizzes
6 videos
Class Activation Maps3m
Fashion MNIST Class Activation Map code walkthrough4m
Saliency5m
GradCAM5m
ZFNet5m
4 readings
Reference: GradCam10m
Reference: ZFNet10m
References 10m
Acknowledgments10m
1 practice exercise
Visualization and Interpretation30m

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About the TensorFlow: Advanced Techniques Specialization

TensorFlow: Advanced Techniques

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