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 moreKL divergence is used to compare probability distribution functions. This article focuses on deriving a closed form solution for KL divergence using in Variational Autoencoders.
Read moreThe goal of this article is to understand and derive the ELBO (Evidence Lower Bound) cost function used in training Variational Autoencoder. The article is designed with an assumption that the readers possess basic understanding of Generative Modelling and Variational Autoencoders.
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 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.
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