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# limitations under the License.Using more sophisticated images with Convolutional Neural Networks
In the previous lesson you saw how to use a CNN to make your recognition of the handwriting digits more efficient. In this lesson you'll take that to the next level, recognizing real images of Cats and Dogs in order to classify an incoming image as one or the other. In particular the handwriting recognition made your life a little easier by having all the images be the same size and shape, and they were all monochrome color. Real-world images aren't like that -- they're in different shapes, aspect ratios etc, and they're usually in color!
So, as part of the task you need to process your data -- not least resizing it to be uniform in shape.
You'll follow these steps:
- Explore the Example Data of Cats and Dogs
- Build and Train a Neural Network to recognize the difference between the two
- Evaluate the Training and Validation accuracy
Explore the Example Data
Let's start by downloading our example data, a .zip of 2,000 JPG pictures of cats and dogs, and extracting it locally in /tmp.