Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. In this post, you will […]

# Search results for "summarization"

## 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 […]

## 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 […]

## Making Predictions with Sequences

Sequence prediction is different from other types of supervised learning problems. The sequence imposes an order on the observations that must be preserved when training models and making predictions. Generally, prediction problems that involve sequence data are referred to as sequence prediction problems, although there are a suite of problems that differ based on the […]

## Attention in Long Short-Term Memory Recurrent Neural Networks

The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very […]

## Techniques to Handle Very Long Sequences with LSTMs

Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only […]

## How To Get Started With Machine Learning in R (get results in one weekend)

How do you get started with machine learning in R? R is a large and complex platform. It is also the most popular platform for the best data scientists in the world. In this post you will discover the step-by-step process that you can use to get started using machine learning for predictive modeling on […]

## Better Understand Your Data in R Using Descriptive Statistics

You must become intimate with your data. Any machine learning models that you build are only as good as the data that you provide them. The first step in understanding your data is to actually look at some raw values and calculate some basic statistics. In this post, you will discover how you can quickly get […]

## How to Use a Machine Learning Checklist to Get Accurate Predictions, Reliably

How do you get accurate results using machine learning on problem after problem? The difficulty is that each problem is unique, requiring different data sources, features, algorithms, algorithm configurations and on and on. The solution is to use a checklist that guarantees a good result every time. In this post you will discover a checklist […]

## Inteview: Discover the Methodology and Mindset of a Kaggle Master

What does it take to do well in competitive machine learning? To really dig into this question, you need to dig into the people that do well. In 2010 I participated in a Kaggle competition to predict the outcome of chess games in the future. It was a fascinating problem because it required you to […]