Author Archive | Jason Brownlee

A Gentle Introduction to Neural Machine Translation

A Gentle Introduction to Neural Machine Translation

One of the earliest goals for computers was the automatic translation of text from one language to another. Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were used for this task, which were replaced in the 1990s with statistical methods. […]

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Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model

Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role […]

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Depiction of Choices in Designing a Checker-Playing Learning System

Why Applied Machine Learning Is Hard

How to Handle the Intractability of Applied Machine Learning. Applied machine learning is challenging. You must make many decisions where there is no known “right answer” for your specific problem, such as: What framing of the problem to use? What input and output data to use? What learning algorithm to use? What algorithm configuration to […]

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A Gentle Introduction to Exploding Gradients in Recurrent Neural Networks

A Gentle Introduction to Exploding Gradients in Neural Networks

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. In this post, you will discover the problem of exploding gradients with deep artificial neural […]

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A Gentle Introduction to Concept Drift in Machine Learning

A Gentle Introduction to Concept Drift in Machine Learning

Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables. This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning. In this post, you will discover the problem […]

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Difference Between Classification and Regression in Machine Learning

Difference Between Classification and Regression in Machine Learning

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 […]

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Encoder-Decoder Models for Text Summarization in Keras

Encoder-Decoder Models for Text Summarization in Keras

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 […]

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