Visualizing Filters of a CNN using TensorFlow

Offered byCoursera

Visualizing Filters of a CNN using TensorFlow
at
Coursera
Overview

Unveiling CNN Filters: Harnessing TensorFlow for Visualization

Duration	1 hour
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Go to Website
Credential	Certificate

Visualizing Filters of a CNN using TensorFlow
at
Coursera
Highlights

Hands-on learning
Guided Project
Expert Guidance

Visualizing Filters of a CNN using TensorFlow
at
Coursera
Course details

Skills you will learn

Python Data visualization

What are the course deliverables?

Implement gradient ascent algorithm
Visualize image features that maximally activate filters of a CNN

More about this course

In this 1-hour guided project, we explore Convolutional Neural Networks (CNNs) using the popular VGG16 model. We employ gradient ascent to visualize filters across different CNN layers.
Utilizing TensorFlow in the Google Colab environment with free GPUs, this hands-on project is for Python-savvy learners with theoretical knowledge of Neural Networks and optimization techniques. Discover how to leverage TensorFlow to visualize CNN filters effectively.

Visualizing Filters of a CNN using TensorFlow
at
Coursera
Curriculum

Introduction

Downloading the Model

Get Submodels

Image Visualization

Training Loop

Final Results

Visualizing Filters of a CNN using TensorFlow
at
Coursera
Faculty details

Amit Yadav

Amit is a Machine Learning Engineer with an interest in building machine learning products. He has led a number of machine learning projects at different companies - ranging from startups to large multinational corporations.