Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data […]

# Search results for "embedding"

## Datasets for Natural Language Processing

You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard datasets that are well understood and widely used so […]

## Promise of Deep Learning for Natural Language Processing

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 […]

## 7 Applications of Deep Learning for Natural Language Processing

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 […]

## 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? […]

## Encoder-Decoder Long Short-Term Memory Networks

Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute […]

## How to Get Reproducible Results with Keras

Neural network algorithms are stochastic. This means they make use of randomness, such as initializing to random weights, and in turn the same network trained on the same data can produce different results. This can be confusing to beginners as the algorithm appears unstable, and in fact they are by design. The random initialization allows […]

## 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 […]

## 8 Inspirational Applications of Deep Learning

It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. […]

## How to Predict Sentiment from Movie Reviews Using Deep Learning (Text Classification)

Sentiment analysis is a natural language processing problem where text is understood, and the underlying intent is predicted. In this post, you will discover how you can predict the sentiment of movie reviews as either positive or negative in Python using the Keras deep learning library. After reading this post, you will know: About the […]