Deep learning defined Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks.

Encoder-Decoder Architectures Encoder-decoder architectures are a broad category of models used primarily for tasks that involve transforming input data into output data of a different form or ...

Neural machine translation (NMT) employs the prevailing deep learning techniques to build a single deep neural network (DNN) that directly maps the input speech utterances of one language to the ...

Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the ...

We dedicate this project to a core deep learning based model for sequence-to-sequence modeling and in particular machine translation: An Encoder-Decoder architecture based on Long-Short Term Memory ...

The transformer model has become one of the main highlights of advances in deep learning and deep neural networks.

Since the deep learning boom has started, numerous researchers have started building many architectures around neural networks. It is often speculated that the neural networks are inspired by neurons ...

Deep Learning (DL) is, in essence, Machine Learning on steroids. It’s a specialized subfield that focuses on algorithms inspired by the structure of the brain, known as artificial neural networks.

Deepfakes are simple to make. A simple overview of the artificial intelligence (AI) behind deepfakes: Generative Adversarial Networks (GANs), Encoder-decoder pairs and First-Order Motion Models.