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This course is part of TensorFlow: Advanced Techniques Specialization
Instructors: Laurence Moroney
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43,237 already enrolled
(515 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
(515 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
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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.
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.
10 videos4 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!
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!
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.
6 videos6 readings1 assignment1 programming assignment4 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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515 reviews
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Reviewed on Dec 27, 2020
Very interesting course and complex content. Perfect place to start if your planning enter into a research field in Computer Vision.
Reviewed on Feb 10, 2021
The whole course was very good! Especially week 4, learned some new concepts in week 4.
Reviewed on Apr 12, 2021
It is awesome course. Direct to the point with great depth in subject.
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