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Image Classification with CNNs using Keras

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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.

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Learn to create and train Convolutional Neural Networks (CNNs) for image classification using Keras and TensorFlow in this 1-hour project-based course. Designed for data science and AI professionals, it offers hands-on learning via Coursera's Rhyme platform, featuring pre-configured cloud desktops with essential tools like Python, Jupyter, and TensorFlow. Ideal for those familiar with Python and CNNs, this course provides instant access to training materials and is best suited for learners in North America. Access the cloud desktop up to 5 times and enjoy unlimited instructional videos. Subscription: Starter.