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. […]
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 […]
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 […]
Convolutional neural networks are now capable of outperforming humans on some computer vision tasks, such as classifying images. That is, given a photograph of an object, answer the question as to which of 1,000 specific objects the photograph shows. A competition-winning model for this task is the VGG model by researchers at Oxford. What is […]
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 […]
The Keras Python library makes creating deep learning models fast and easy. The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. The functional API in Keras is an alternate way […]
Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some […]
The promise of deep learning in the field of natural language processing is the better performance by models that may require more data but less linguistic expertise to train and operate. There is a lot of hype and large claims around deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results on […]
The field of natural language processing is shifting from statistical methods to neural network methods. There are still many challenging problems to solve in natural language. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific language problems. It is not just the performance of deep learning models on benchmark problems that is most […]
Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford […]
