Image Super-Resolution using CNN with Keras in Python
Image Super-Resolution using CNN with Keras in Python, available at $34.99, has an average rating of 3.75, with 32 lectures, based on 16 reviews, and has 1055 subscribers.
You will learn about Understand the fundamentals of Efficient Sub-pixel Convolutional Neural Network (CNN) Build and train a the super-resolution model using Keras with Tensorflow as a backend using Google Colab Assess the performance of trained model Learn to use the trained model to predict the high-resolution image of a new set of image data This course is ideal for individuals who are Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image super-resolution code It is particularly useful for Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image super-resolution code.
Enroll now: Image Super-Resolution using CNN with Keras in Python
Summary
Title: Image Super-Resolution using CNN with Keras in Python
Price: $34.99
Average Rating: 3.75
Number of Lectures: 32
Number of Published Lectures: 32
Number of Curriculum Items: 32
Number of Published Curriculum Objects: 32
Original Price: ₹799
Quality Status: approved
Status: Live
What You Will Learn
- Understand the fundamentals of Efficient Sub-pixel Convolutional Neural Network (CNN)
- Build and train a the super-resolution model using Keras with Tensorflow as a backend using Google Colab
- Assess the performance of trained model
- Learn to use the trained model to predict the high-resolution image of a new set of image data
Who Should Attend
- Beginners starting out to the field of Deep Learning
- Industry professionals and aspiring data scientists
- People who want to know how to write their image super-resolution code
Target Audiences
- Beginners starting out to the field of Deep Learning
- Industry professionals and aspiring data scientists
- People who want to know how to write their image super-resolution code
Welcome to the “Image Super-Resolution using CNN with Keras in Python” course. In this project, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend from scratch, and you will learn to trainCNNs to enhance the quality of images significantly. Our neural network will create high-resolution images from low-resolution images. Please note that you don’t need a high-powered workstation to learn this course. We will be carrying out the entire project in the Google Colab environment, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. By the end of this course, you will be able to build and train the deep learning model using your image dataset. After that, you will also be able to use the model to predicthigh-resolution imageson new images and visualisethem. This image super-resolution course is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your following job interview. This course is designed most straightforwardly to utilise your time wisely.
Happy learning.
Course Curriculum
Chapter 1: Fundamentals
Lecture 1: Introduction
Lecture 2: Artificial Intelligence
Lecture 3: Machine Learning
Lecture 4: Deep Learning
Lecture 5: What is Image Super-Resolution?
Lecture 6: How Image Super-Resolution is done?
Lecture 7: Efficient Sub-pixel Convolutional Neural Network (ESPCN)
Chapter 2: Building, Evaluating and Predicting Super-Resolution Model
Lecture 1: Download Dataset
Lecture 2: What is inside data folder?
Lecture 3: Super-Resolution Python Code
Lecture 4: What is the .h5 file?
Lecture 5: What is inside test folder?
Lecture 6: What is inside prediction folder?
Lecture 7: Enabling GPU in Google Colab
Lecture 8: Is GPU connected to Colab notebook?
Lecture 9: Connect Google Colab with Google Drive
Lecture 10: Import Python Libraries
Lecture 11: Creating Training Data Generator
Lecture 12: Creating Validation Data Generator
Lecture 13: Normalize the Pixels for Training and Validation Images
Lecture 14: Visualize Sample Images
Lecture 15: Process the Input Images and Visualize it
Lecture 16: Build a CNN Model Architecture
Lecture 17: Define Utility Functions to Monitor our Results
Lecture 18: Peak Signal to Noise Ratio (PSNR)
Lecture 19: Dataset of Test Image Paths
Lecture 20: Define Callbacks
Lecture 21: Visualize Model Architecture
Lecture 22: Model Compilation
Lecture 23: Training the Model
Lecture 24: Model Testing / Evaluation
Lecture 25: Prediction
Instructors
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Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 3 votes
- 3 stars: 5 votes
- 4 stars: 1 votes
- 5 stars: 7 votes
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