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学生对 Coursera Project Network 提供的 Visualizing Filters of a CNN using TensorFlow 的评价和反馈

35 个评分
2 条评论


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....

1 - Visualizing Filters of a CNN using TensorFlow 的 2 个评论(共 2 个)

创建者 Pooja.Bidwai p

Dec 14, 2021


创建者 Fabian B

Apr 14, 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.