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In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model.
We will be using TensorFlow as our machine learning framework. The project uses the Google Colab environment which is a fantastic tool for creating and running Jupyter Notebooks in the cloud, and Colab even provides free GPUs for your notebooks.
You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like gradient descent but want to understand how to use the TensorFlow to visualize various filters of a CNN.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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1 - 7 von 7 Bewertungen für Visualizing Filters of a CNN using TensorFlow
von Kenneth N
•4. Juli 2022
very well prepared and explained. but colab is slow
von Shadi Q
•18. Jan. 2023
Clear and easy explanation
von Pooja.Bidwai p
•14. Dez. 2021
awesome
von Fabian B
•14. Apr. 2022
instructor explains everything clearly, but an actual application was missing. a quick cats and dogs comparison on how to infer filter activation would have been helpful.
von Sanskriti S
•20. Juli 2022
the course wss helpful but more ws expected in terms of explanation and examples
von Hemil P
•9. Nov. 2022
Not explaining everything, just giving the overview.
von Javier G
•24. Mai 2022
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