the layered image structure of an MRI scan. It can however also be height or number of layers, in e.g. In videos, which are essentially many images stacked together, time is this third axis. Rather, the height or time dimension is also important. How these Conv2D networks work has been explained in another blog post.įor many applications, however, it’s not enough to stick to two dimensions. We all know about the computer vision applications which allow us to perform object detection, to name just one. The cover image is courtesy of David de la Iglesia Castro, the creator of the 3D MNIST dataset. Primarily, these networks have been applied to two-dimensional data: data with two axes (x and y), such as images. These past few years, convolutional neural networks have become known for the boost they gave to machine learning, or artificial intelligence in a broader sense.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |