Category: Machine Learning

Muhammad Asad Iqbal Khan summarizes a document:

Text summarization represents a sophisticated evolution of text generation, requiring a deep understanding of content and context. With encoder-decoder transformer models like DistilBart, you can now create summaries that capture the essence of longer text while maintaining coherence and relevance.

In this tutorial, you’ll discover how to implement text summarization using DistilBart. You’ll learn through practical, executable examples, and by the end of this guide, you’ll understand both the theoretical foundations and hands-on implementation details. After completing this tutorial, you will know:

Click through for the article.

Matthew Mayo does a bit of kernel work:

Kernel methods are a powerful class of machine learning algorithm that allow us to perform complex, non-linear transformations of data without explicitly computing the transformed feature space. These methods are particularly useful when dealing with high-dimensional data or when the relationship between features is non-linear.

Kernel methods rely on the concept of a kernel function, which computes the dot product of two vectors in a transformed feature space without explicitly performing the transformation. This is known as the kernel trick. The kernel trick allows us to work in high-dimensional spaces efficiently, making it possible to solve complex problems that would be computationally infeasible otherwise.

Read on for the pros and cons of kernel methods and a pair of techniques that use them.