Visualizing Filters of a CNN using TensorFlow

In this 1-hour guided project, you'll delve into using TensorFlow to visualize filters from different layers of a Convolutional Neural Network (CNN). With VGG16 model as the foundation, you'll implement the gradient ascent algorithm to visualize images that maximally activate specific filters.

• Explore the practical application of Convolutional Neural Networks (CNN) and optimization algorithms like gradient descent.
• Utilize the Google Colab environment to create and run Jupyter Notebooks in the cloud, with access to free GPUs for your projects.
• Gain hands-on experience in implementing the gradient ascent algorithm to visualize image features that maximally activate filters of a CNN.
• Perfect for learners with theoretical understanding of Neural Networks and CNN, seeking a practical understanding of using TensorFlow for CNN visualization.