Transduction or transductive learning are terms you may come across in applied machine learning. The term is being used with some applications of recurrent neural networks on sequence prediction problems, like some problems in the domain of natural language processing. In this post, you will discover what transduction is in machine learning. After reading this […]

# Archive | Deep Learning for Natural Language Processing

## Primer on Neural Network Models for Natural Language Processing

Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? […]

## Oxford Course on Deep Learning for Natural Language Processing

Deep Learning methods achieve state-of-the-art results on a suite of natural language processing problems What makes this exciting is that single models are trained end-to-end, replacing a suite of specialized statistical models. The University of Oxford in the UK teaches a course on Deep Learning for Natural Language Processing and much of the materials for […]

## Review of Stanford Course on Deep Learning for Natural Language Processing

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 […]

## Top Books on Natural Language Processing

Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with […]

## Text Generation With LSTM Recurrent Neural Networks in Python with Keras

Recurrent neural networks can also be used as generative models. This means that in addition to being used for predictive models (making predictions), they can learn the sequences of a problem and then generate entirely new plausible sequences for the problem domain. Generative models like this are useful not only to study how well a […]

## Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time, and the task is to predict a category for the sequence. This problem is difficult because the sequences can vary in length, comprise a very large vocabulary of input symbols, and may require the model to learn […]