Convolutional neural networks (CNNs) are the state of the art when it comes to computer vision. As such we will build a CNN model to distinguish images of cats from those of dogs by using the Dogs vs. Cats Redux: Kernels Edition dataset.

Pre-trained deep CNNs typically generalize easily to different but similar datasets with the help of transfer learning. The reason is simple: the filters present in the earlier convolutional layers of a CNN usually capture low-level features such as straight lines, whereas higher-level filters recognizing complex objects such as faces are activated deeper in the network. As such it is possible to directly use the training weights associated with shape recognition and retrain only the deepest layers of the network - a procedure called finetuning or transfer learning - to perform classification tasks on different types of images.

For this competition we follow the process described in the deep learning course fast.ai Read more →