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 […] ...

Deep Learning neural networks can work well for several tasks today. We can use deep neural networks for image recognition, object detection, segmentation, speech, NLP and much more. But there is one problem with deep neural networks. They are bad at handling noise during real-world testing. The generalization power of deep neural networks reduces drastically […] ...

Updated: March 25, 2020. In this post, we will be denoising text image documents using deep learning autoencoder neural network. And we will not be using MNIST, Fashion MNIST, or the CIFAR10 dataset. In fact, we will be using one of the past Kaggle competition data for this autoencoder deep learning project. More specifically, we […] ...