![]() Many businesses avoid the use of neural network models due to a lack of such explainability.īut we can answer some of the questions that we asked above. ![]() In regard to deep neural networks, explainability is still a widely researched field. Data scientists and business managers also need to know why a model took a particular decision as it can radically affect a big organization. When dealing with machine learning models like random forests, or decision trees, we can explain many of its decision making procedure. This is in contrast to machine learning model explainability. What did the convolutional neural network see in the intermediate layers?Īfter many years of research, we can answer some of the questions, and some other questions partially.Why did the image classify a cat as a bird?.How did the neural network decide that the image is a cat?.Image recognition, object detection, and semantic segmentation are only some of the applications of convolutional neural networks among many more.īut when it comes down to how a convolutional neural network decides what an image actually is, things become trickier. Convolutional neural networks have proved to provide many state-of-the-art solutions and benchmarks in deep learning and computer vision. When dealing with image data in deep learning, then convolutional neural networks (CNN) are the go-to architectures.
0 Comments
Leave a Reply. |