In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.
Get the Data
Run the following cell to download the CIFAR-10 dataset for python.Read more
Neural Art Demo
This demo is an implementation of the algorithm in the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.
This algorithm transfers 'artistic style' from one image to the other. For example, you can make your favourite photo looks like van Gogh's painting.
First, we need to images to work with. We extract the 'style' image's style and transfer it to the 'content' image.
The default images are photo of buildings and van Gogh's painting.
You can swap them with any images on the internet you like.Read more
Object Detection Demo
Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the installation instructions before you start.Read more