Image Classification with CNNs using Keras
In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with python and convolutional neural networks. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.
由 SS 提供2021年5月13日
Really I learned a lot of information about CNN using Keras
由 DJ 提供2020年5月13日
very nice and useful courser for learning practical
由 SJ 提供2020年6月5日
Course was good but rhyme interface was bad and needs an improvement
由 SB 提供2020年6月2日
Really enjoyed learning from Amit Yadav. He promptly answers any query posted in the discussion forum. Looking forward to learning more from him.