A Gentle Introduction to Calculating the BLEU Score for Text in Python

Last Updated on December 19, 2019 BLEU, or the Bilingual Evaluation Understudy, is a score for comparing a candidate translation of text to one or more reference translations. Although developed for translation, it can be used to evaluate text generated for a suite of natural language processing tasks. In this tutorial, you will discover the BLEU score for evaluating and scoring candidate text using the NLTK library in Python. After completing this tutorial, you will know: A gentle introduction to […]

Read more

A Gentle Introduction to Deep Learning Caption Generation Models

Last Updated on August 7, 2019 Caption generation is the challenging artificial intelligence problem of generating a human-readable textual description given a photograph. It requires both image understanding from the domain of computer vision and a language model from the field of natural language processing. It is important to consider and test multiple ways to frame a given predictive modeling problem and there are indeed many ways to frame the problem of generating captions for photographs. In this tutorial, you […]

Read more

How to Use Small Experiments to Develop a Caption Generation Model in Keras

Last Updated on September 3, 2020 Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right order. Recently, deep learning methods have achieved state of the art results on examples of this problem. It can […]

Read more

A Gentle Introduction to Text Summarization

Last Updated on August 7, 2019 Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster. In this post, you will discover the problem of text summarization in natural language processing. After reading this post, you will know: Why text summarization is […]

Read more

Encoder-Decoder Deep Learning Models for Text Summarization

Last Updated on August 7, 2019 Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Recently deep learning methods have proven effective at the abstractive approach to text summarization. In this post, you will discover three different models that build on top of the effective Encoder-Decoder architecture developed for sequence-to-sequence prediction in machine translation. After reading this post, you will know: The Facebook AI Research model that uses the Encoder-Decoder model with a […]

Read more

How to Prepare News Articles for Text Summarization

Last Updated on August 7, 2019 Text summarization is the task of creating a short, accurate, and fluent summary of an article. A popular and free dataset for use in text summarization experiments with deep learning methods is the CNN News story dataset. In this tutorial, you will discover how to prepare the CNN News Dataset for text summarization. After completing this tutorial, you will know: About the CNN News dataset and how to download the story data to your […]

Read more

What is Teacher Forcing for Recurrent Neural Networks?

Last Updated on August 14, 2019 Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the ground truth from a prior time step as input. It is a network training method critical to the development of deep learning language models used in machine translation, text summarization, and image captioning, among many other applications. In this post, you will discover the teacher forcing as a method for training recurrent neural networks. After reading this […]

Read more

Encoder-Decoder Models for Text Summarization in Keras

Last Updated on August 7, 2019 Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization. It can be difficult to apply this architecture in the Keras deep learning library, given some of the flexibility sacrificed to make the library clean, simple, and easy to use. In this […]

Read more

Difference Between Classification and Regression in Machine Learning

Last Updated on May 22, 2019 There is an important difference between classification and regression problems. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. I often see questions such as: How do I calculate accuracy for my regression problem? Questions like this are a symptom of not truly understanding the difference between classification and regression and what accuracy is trying to measure. In this tutorial, you will discover the differences between classification and regression. […]

Read more

How to Visualize a Deep Learning Neural Network Model in Keras

Last Updated on September 11, 2019 The Keras Python deep learning library provides tools to visualize and better understand your neural network models. In this tutorial, you will discover exactly how to summarize and visualize your deep learning models in Keras. After completing this tutorial, you will know: How to create a textual summary of your deep learning model. How to create a graph plot of your deep learning model. Best practice tips when developing deep learning models in Keras. […]

Read more
1 807 808 809 810 811 905