Neural Machine Translation is the use of Deep Neural Networks for translating a text from one language (source language) to its counterpart in other language (target language).
Read moreIn many real-world data applications, often we encounter scenarios where each data point may belong to multiple classes. A multilabel classifier is trained to predict the K most likely classes among N possible classes. The article focuses on solving multi-label text classification problems using the Hierarchical Attention Network.
Read moreWith the advent of GANs and its variations, Generative Modelling has picked up pace in deep learning research. This article is aimed at providing a gentle introduction to Generative modeling by leveraging Multivariate Normal Distribution.
Read moreAs a part of "Getting Acquainted with Deep Learning Frameworks" series, in the article we shall explore Pytorch Library. Pytorch is a deep learning library developed by Facebook Researchers. The focus of this article will be to highlight the steps involved in training a multiclass classifier using Pytorch.
Read moreGetting started with deep learning frameworks often involves a steep learning curve. This article is aimed at providing a gentle introduction to building DNN models with Keras which can be scaled and customized as per dataset. The focus will be on understanding the syntax and good practices involved in building a complex DNN model rather than achieving accuracy.
Read moreImage classification using deep learning and its applications has been proving its worth across business verticals.However, building an image centric AI product is often marred by Unavailability of large amounts of data, poor quality of images, etc. Often the metadata / textual data associated with these images are ignored. In this article, we shall try and build a DL model that can leverage image as well as textual data.
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