Last Updated on August 7, 2019 Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role of the recurrent neural network in addressing the problem. In this post, you will discover the inject and merge […]

Last Updated on August 7, 2019 One of the earliest goals for computers was the automatic translation of text from one language to another. Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were used for this task, which were replaced in the 1990s with statistical methods. More recently, deep neural network models achieve state-of-the-art results in a field that is aptly named neural machine translation. […]

Last Updated on August 7, 2019 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 the two seminal examples of the encoder-decoder model for neural machine translation. After reading this post, you will know: […]

Last Updated on August 7, 2019 The encoder-decoder architecture for recurrent neural networks is achieving state-of-the-art results on standard machine translation benchmarks and is being used in the heart of industrial translation services. The model is simple, but given the large amount of data required to train it, tuning the myriad of design decisions in the model in order get top performance on your problem can be practically intractable. Thankfully, research scientists have used Google-scale hardware to do this work […]

Last Updated on June 3, 2020 Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation […]

Last Updated on April 30, 2020 Machine translation is the challenging task of converting text from a source language into coherent and matching text in a target language. Neural machine translation systems such as encoder-decoder recurrent neural networks are achieving state-of-the-art results for machine translation with a single end-to-end system trained directly on source and target language. Standard datasets are required to develop, explore, and familiarize yourself with how to develop neural machine translation systems. In this tutorial, you will […]

Last Updated on September 3, 2020 Develop a Deep Learning Model to AutomaticallyTranslate from German to English in Python with Keras, Step-by-Step. Machine translation is a challenging task that traditionally involves large statistical models developed using highly sophisticated linguistic knowledge. Neural machine translation is the use of deep neural networks for the problem of machine translation. In this tutorial, you will discover how to develop a neural machine translation system for translating German phrases to English. After completing this tutorial, […]

Last Updated on September 3, 2020 A standard deep learning model for text classification and sentiment analysis uses a word embedding layer and one-dimensional convolutional neural network. The model can be expanded by using multiple parallel convolutional neural networks that read the source document using different kernel sizes. This, in effect, creates a multichannel convolutional neural network for text that reads text with different n-gram sizes (groups of words). In this tutorial, you will discover how to develop a multichannel […]