Tag: Neural Networks

Updated: March 25, 2020. One of the applications of deep learning autoencoders is image reconstruction. But it is not necessary that the input images will always be clean. Sometimes, the input images for autoencoders can be noisy. In that case, the deep learning autoencoder has to denoise the input images, get the hidden code representation, […] ...

Updated on 14 November 2020. In this article, we take a hands-on approach to building deep learning autoencoders. We will implement deep autoencoders using linear layers with PyTorch. What Will We Cover in this Article? A brief introduction to autoencoders. The approach for this article. Building a deep autoencoder with PyTorch linear layers. We will also take a look at all […] ...

Updated: March 25, 2020. If you are into deep learning, then till now you may have seen many cases of supervised deep learning using neural networks. In this article, we will take a dive into an unsupervised deep learning technique using neural networks. Specifically, we will learn about autoencoders in deep learning. What Will We Cover in this Article? – What […] ...

In this article, we will get to learn the basics of neural networks and how to build them using PyTorch. Essentially we will use the torch.nn package and write Python class to build a neural network in PyTorch. This is one of the most flexible and best methods to do so. This is the third […] ...

In this article, we will take a look at how we can use image augmentation in deep learning. Data augmentation is a very useful technique when dealing with image data. Image augmentation is most helpful when the dataset is small. We can also benefit from image augmentation when we are not able to find any […] ...