Last Updated on August 7, 2019 You cannot feed raw text directly into deep learning models. Text data must be encoded as numbers to be used as input or output for machine learning and deep learning models. The Keras deep learning library provides some basic tools to help you prepare your text data. In this tutorial, you will discover how you can use Keras to prepare your text data. After completing this tutorial, you will know: About the convenience methods […]

Last Updated on September 3, 2020 Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this tutorial, you will discover how to use word embeddings for deep learning in Python with Keras. After completing this […]

Last Updated on September 3, 2020 Word embeddings are a modern approach for representing text in natural language processing. Word embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing problems like machine translation. In this tutorial, you will discover how to train and load word embedding models for natural language processing applications in Python using Gensim. After completing this tutorial, you will know: How to train your own […]

Last Updated on August 7, 2019 The bag-of-words model is a way of representing text data when modeling text with machine learning algorithms. The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification. In this tutorial, you will discover the bag-of-words model for feature extraction in natural language processing. After completing this tutorial, you will know: What the bag-of-words model is and why it is needed to […]

Last Updated on August 7, 2019 Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation. They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. In this post, you will discover the word embedding approach for representing text data. After completing this post, you will know: What the word embedding approach […]

Last Updated on August 7, 2019 Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation. In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. Applications and extensions to the attention mechanism. Kick-start your project with my new book Deep Learning for Natural Language […]

Last Updated on August 14, 2020 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 ready for modeling. In this tutorial, you will discover how to prepare movie review text data for sentiment analysis, […]

Last Updated on August 7, 2019 You cannot go straight from raw text to fitting a machine learning or deep learning model. You must clean your text first, which means splitting it into words and handling punctuation and case. In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods really depends on your natural language processing task. In this tutorial, you will discover how you can clean and […]