Visualizing Filters of a CNN using TensorFlow

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Coursera Project Network
在此免費指導項目中,您將:

Implement gradient ascent algorithm

Visualize image features that maximally activate filters of a CNN

在面試中展現此實踐經驗

Clock1 hour
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

必備條件

Prior experience in Python, theoretical understanding of Convolutional Neural Networks and optimization algorithms like gradient descent.

您要培養的技能

  • Deep Learning
  • Artificial Neural Network
  • Convolutional Neural Network
  • Machine Learning
  • Tensorflow

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction

  2. Downloading the Model

  3. Get Submodels

  4. Image Visualization

  5. Training Loop

  6. Final Results

指導項目工作原理

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在分屏視頻中,您的授課教師會為您提供分步指導

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