Recurrent neural networks (RNNs) are capable of learning features and long term
dependencies from sequential and time-series data. The RNNs have a stack of non-linear …

Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data
has become important as many organizations both public and private have been collecting …

Language modelling provides a step towards intelligent communication systems by
harnessing large repositories of written human knowledge to better predict and understand …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Incorporating second-order curvature information into machine learning optimization
algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Deep learning models (aka Deep Neural Networks) have revolutionized many fields
including computer vision, natural language processing, speech recognition, and is being …

We describe a simple neural language model that relies only on character-level inputs.
Predictions are still made at the word-level. Our model employs a convolutional neural …

Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is
turning into a substantial challenge for financial institutions and service providers, thus …

Social media is a more common and powerful platform for communication to share views
about any topic or article, which consequently leads to unstructured toxic, and hateful …