If we are talking in terms of rows and columns, then the below dummy data gives us a pretty good idea. In a single-label classification problem, we have a bunch of features and a single output value based on what the dataset consists of. The MNIST, Fashion MNIST, and CIFAR10 datasets are some of the classic examples for single-label image classification if you are starting out with deep learning and neural networks. And you must have tackled many problems for labeling images and other datasets into a single label. In the field of deep learning, single-label classification is pretty common. A Brief on Single and Multi-Label Classification using Deep Learning Models We will see how to implement the same using the PyTorch deep learning framework in the subsequent tutorials. And I will surely consider your feedback for that. Also, you may find out if I missed something. Your suggestions, thoughts, and feedback will be much valuable to me. If you are already well-versed with the topic, maybe you can still go through the post. Note: Many of the readers may be well aware of multi-label classification and multi-head neural networks. An example of a deep learning model having multiple output heads instead of a single output head.
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