This article offers an empirical exploration on the use of character-level convolu-tional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

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 best practices to consider when developing deep learning models for text classification. Best Practices for Document Classification with Deep Learning Photo by storebukkebruse, some rights reserved. Take my free 7-day email crash course now (with code).

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks. Papers published at the Neural Information Processing Systems Conference.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research.

Deep Learning, one of the subfields of Machine Learning and Statistical Learning has been advancing in impressive levels in the past years. Cloud computing, robust open source tools and vast amounts of available data have been some of the levers for these impressive breakthroughs. The criteria used to select the 20 top papers is by using citation counts from academic.microsoft.com. It is important to mention that these metrics are changing rapidly so the citations valued must be considered as the numbers when this article was published. In this list of papers more than 75% refer to deep learning and neural networks, specifically Convolutional Neural Networks (CNN).

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 best practices to consider when developing deep learning models for text classification. Best Practices for Document Classification with Deep Learning Photo by storebukkebruse, some rights reserved. The modus operandi for text classification involves the use of a word embedding for representing words and a Convolutional Neural Network (CNN) for learning how to discriminate documents on classification problems.

One of the common natural language understanding problems is text classification. Over last few decades, machine learning researchers have been moving from the simplest "bag of words" model to more sophisticated models for text classification. Bag of words model uses only information about which words are used in the text. Adding TFIDF to the bag of words helps to track relevancy of each word to the document. Bag of n-grams enables using partial information about structure of the text. Recurrent neural networks, like LSTM, can capture dependencies between words even if they are far from each other.

Chatbots are on the rise. Startups are building chatbots, platforms, APIs, tools, analytics. Microsoft, Google, Facebook introduce tools and frameworks, and build smart assistants on top of these frameworks. Multiple blogs, magazines, podcasts report on news in this industry, and chatbot developers gather on meetups and conferences. I have been working on chatbot software for a while, and I have been looking on what is going on in the industry. In this article, I will dive into architecture of chatbots.