Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Recently deep learning methods have proven effective at the abstractive approach to text summarization. In this post, you will discover three different models that build on top of the effective Encoder-Decoder architecture developed for sequence-to-sequence prediction in machine translation. […]
Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right […]
Caption generation is the challenging artificial intelligence problem of generating a human-readable textual description given a photograph. It requires both image understanding from the domain of computer vision and a language model from the field of natural language processing. It is important to consider and test multiple ways to frame a given predictive modeling problem […]
Deep Learning for Natural Language Processing Develop Deep Learning Models for your Natural Language Problems Working with Text is… important, under-discussed, and HARD We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances. Every day, I get questions asking how to develop machine learning models for text data. Working […]
Automatic photo captioning is a problem where a model must generate a human-readable textual description given a photograph. It is a challenging problem in artificial intelligence that requires both image understanding from the field of computer vision as well as language generation from the field of natural language processing. It is now possible to develop […]
Captioning an image involves generating a human readable textual description given an image, such as a photograph. It is an easy problem for a human, but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. Recently, deep learning methods have […]
A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can […]
Deep Learning for NLP Crash Course. Bring Deep Learning methods to Your Text Data project in 7 Days. We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances. Working with text is hard as it requires drawing upon knowledge from diverse domains such as linguistics, machine learning, statistical […]
Language modeling involves predicting the next word in a sequence given the sequence of words already present. A language model is a key element in many natural language processing models such as machine translation and speech recognition. The choice of how the language model is framed must match how the language model is intended to […]
Language modeling is central to many important natural language processing tasks. Recently, neural-network-based language models have demonstrated better performance than classical methods both standalone and as part of more challenging natural language processing tasks. In this post, you will discover language modeling for natural language processing. After reading this post, you will know: Why language […]
