This tutorial is designed for anyone looking for an understanding of how recurrent neural networks (RNN) work and how to use them via the Keras deep learning library. While the Keras library provides all the methods required for solving problems and building applications, it is also important to gain an insight into how everything works. […]
When it comes to sequential or time series data, traditional feedforward networks cannot be used for learning and prediction. A mechanism is required to retain past or historical information to forecast future values. Recurrent neural networks, or RNNs for short, are a variant of the conventional feedforward artificial neural networks that can deal with sequential […]
Deep learning networks have gained immense popularity in the past few years. The “attention mechanism” is integrated with deep learning networks to improve their performance. Adding an attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization, and similar applications. This tutorial shows how to add […]
The encoder-decoder architecture for recurrent neural networks is the standard neural machine translation method that rivals and in some cases outperforms classical statistical machine translation methods. This architecture is very new, having only been pioneered in 2014, although, has been adopted as the core technology inside Google’s translate service. In this post, you will discover […]
Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the ground truth from a prior time step as input. It is a network training method critical to the development of deep learning language models used in machine translation, text summarization, and image captioning, among many other applications. In […]
The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Attention is an extension to the encoder-decoder model that improves the performance of the approach on longer sequences. Global attention is a simplification of attention that may be easier to implement in declarative deep […]
Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation. In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. […]
Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]
The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very […]
