Category: Computer Vision

In this article, we train a ResNet34 deep neural network on the Caltech101 computer vision dataset. We use transfer learning and PyTorch deep learning library to achieve 95% test accuracy. ...

In computer vision based deep learning, the amount of image plays a crucial role in building high accuracy neural network models. When we do not have enough images, we can always rely on image augmentation techniques in deep learning. Image augmentation in deep learning can substantially increase the size of our dataset. This can help […] ...

In this article, we will get hands-on experience on how to build robust deep neural networks by adding noise to the input data. One of my previous articles was Adding Noise for Robust Deep Neural Network Models. It explained how neural networks suffer while generalizing when we add noise to the data. And how we […] ...

PyTorch helps in carrying out deep learning projects and experiments with much ease. But we can improve the deep learning experience even more by tracking our training results, images, graphs and plots. For that, we can use TensorBoard. TensorBoard really eases out the task of keeping track of our deep learning projects. In this article, […] ...

In this article, we will get to know how to add noise to image data for data augmentation in deep learning. So, we will be adding noise to image data for deep learning image augmentation. Noise in the data can seem problematic for deep learning and neural networks in particular. Some of the problems that […] ...