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Understanding vectors in Word2Vector model • /r/MachineLearning

@machinelearnbot

In general, the goal of the loss function is to maximise the dot product between input vector and output vector while minimise the dot product between the input vector and other random vector. So this will make vectors corresponding to input and output word (context word) become more similar. With CBOW, the idea is kind of the same but with a different formulation.


Giveaway: Win an Anki Vector

PCWorld

Anki's Vector is an adorable companion that's packed with tech and personality. By simply speaking to Vector you can play games, have it take your picture, or ask it the weather via Alexa integration. As mentioned in our review, Vector is basically the modern version of the Tamagotchi virtual pet. He'll make you laugh, demand your attention, and just make you feel less lonely when you're at home by yourself. But if you found the $250 price tag hard to swallow, have no fear--we secured a Vector from Anki, and it could be yours!


Chess2vec: Learning Vector Representations for Chess

arXiv.org Artificial Intelligence

We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback. The fundamental challenge for machine learning based chess programs is to learn the mapping between chess positions and optimal moves [5, 3, 7]. A chess position is a description of where pieces are located on the chessboard. In learning, chess positions are typically represented as bitboard representations [1]. A bitboard is a 8 8 binary matrix, same dimensions as the chessboard, and each bitboard is associated with a particular piece type (e.g.


Learning Vector Quantization for Machine Learning - Machine Learning Mastery

#artificialintelligence

A downside of K-Nearest Neighbors is that you need to hang on to your entire training dataset. The Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that lets you choose how many training instances to hang onto and learns exactly what those instances should look like. In this post you will discover the Learning Vector Quantization algorithm. This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems.


Gentle Introduction to Vector Norms in Machine Learning - Machine Learning Mastery

@machinelearnbot

Calculating the length or magnitude of vectors is often required either directly as a regularization method in machine learning, or as part of broader vector or matrix operations.