From the course: Deep Learning: Image Recognition

Unlock the full course today

Join today to access over 22,500 courses taught by industry experts or purchase this course individually.

Exploring the CIFAR-10 data set

Exploring the CIFAR-10 data set - Python Tutorial

From the course: Deep Learning: Image Recognition

Start my 1-month free trial

Exploring the CIFAR-10 data set

- [Instructor] To train neural networks to perform accurately, you need large amounts of training data. Since it's difficult to collect thousands of training images, researches build data sets and share them with each other. For our first image recognition project, we'll be using the CIFAR-10 dataset. This dataset includes thousands of pictures of 10 different kinds of objects, like airplanes, automobiles, birds, and so on. Each image in the dataset includes a matching label so we know what kind of image it is. Using this dataset, we can train our neural network to recognize any of these 10 different kinds of object. Before we build an image recognition model, the first step is to look through the training data that we are working with. We wanna check for bad or unexpected training data. Bad training data is a very common source of problems. For example, imagine that you take millions of photographs and ask volunteers to label them for you. This is called crowd sourcing and is a…

Contents