machine learning - What is a multi-headed model? And what …
May 6, 2019 · If you call ConvLearner.pretrained, CovnetBuilder will build a network with appropriate head to your data in Fast.ai (if you are working on a classification problem, it will create a head with a cross entropy loss, if you are working on a regression problem, it will create a head suited to that).
mlpack - Home
mlpack. fast, header-only C++ machine learning library. with bindings to Python, Julia, R, Go, and the command-line. Written in C++ and built on Armadillo, ensmallen, and cereal. Aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. Read the release announcement.
a fast, header-only machine learning library - GitHub
mlpack is an intuitive, fast, and flexible header-only C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers.
Its efficient implementations of common and cutting-edge machine learning algorithms have been used in a wide variety of scientific and industrial applications. This paper overviews mlpack 4, a significant upgrade over its predecessor.
Attention (machine learning) - Wikipedia
Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence.
Transformer (deep learning architecture) - Wikipedia
A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in the 2017 paper "Attention Is All You Need". [1]
yixuan/MiniDNN: A header-only C++ library for deep neural networks - GitHub
MiniDNN is a header-only library implemented purely in C++98, whose only dependency, Eigen, is also header-only. These features make it easy to embed MiniDNN into larger projects with a broad range of compiler support.
machine learning is important. Of course, we have already mentioned that the achievement of learning in machines might help us understand how animals and humans learn. But there are important engineering reasons as well. Some of these are:
mlpack is a general-purpose machine learning library, targeting both academic and commercial use; for instance, data scientists who need eficiency and ease of deployment, or, e.g., by researchers who need flexibility and extensibility.
mlpack - GitHub
a fast, header-only C++ machine learning library. mlpack has 18 repositories available. Follow their code on GitHub.