Image Classification with CNNs using Keras
12.333 ya inscrito
12.333 ya inscrito
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.
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por SJ5 de jun. de 2020
Course was good but rhyme interface was bad and needs an improvement
por BM19 de jul. de 2020
Perfect project for polishing you deep learning skills mainly CNN and getting introduced to using dataset available globally.
por PA18 de may. de 2020
it was short n up to the mark, fully hands on and i came to know many new terms and their working. as it is a 1 hr assignment, this is just sufficient and satisfactory for beginners.
por MA28 de may. de 2020
it is very useful for my career development. I like this very much