Author: Jeremy Jordan Translation by Machine Heart Contributors: Huang Xiaotian, Xu Di Every machine learning researcher faces the challenge of hyperparameter tuning, and during this tuning process, the adjustment of the learning rate is a crucial part. The learning rate represents the speed at which information accumulates over time in a neural network. Ideally, we … Read more
Machine Learning A magnificent history of artificial intelligence development The victory of AlphaGo, the success of autonomous driving, and breakthroughs in pattern recognition have repeatedly stimulated our nerves with the rapid development of artificial intelligence. As the core of artificial intelligence, machine learning has also attracted much attention in this rapid advancement, shining brightly. Today, … Read more
Since Tomas Mikolov from Google proposed Word2Vec in “Efficient Estimation of Word Representation in Vector Space”, it has become a fundamental component of deep learning in natural language processing. The basic idea of Word2Vec is to represent each word in natural language as a short vector with a unified meaning and dimension. As for what … Read more
Summary of Word2vec Reference Materials Let me briefly describe my deep dive into Word2vec: I first looked at Mikolov’s two original papers on Word2vec, but found myself still confused after reading them. The main reason is that these papers omit too much theoretical background and derivation details. I then revisited Bengio’s 2003 JMLR paper and … Read more
Excerpt from Sebastian Ruder Blog Author: Sebastian Ruder Translated by: Machine Heart Contributors: Terrence L This article is Part 3 of the Word Embedding Series, introducing the popular word embedding model Global Vectors (GloVe). To read Part 2, click on Technical | Word Embedding Series Part 2: Comparing Several Methods of Approximate Softmax in Language … Read more
Word embeddings represent a word with a numerical vector, which is different from the IDs used in Tokenization. Word embedding vectors carry more semantic information. This article will illustrate Word2Vec: a method for word embeddings. This series also includes illustrations of Tokenization, Transformer, GPT2, and BERT. If you want to learn about Tokenization, please see … Read more
Machine Learning Algorithms and Natural Language Processing Recommendations Source: https://zhuanlan.zhihu.com/p/58425003 Author: Xiao Chuan Ryan [Introduction to Machine Learning Algorithms and Natural Language Processing]BERT did not come out of nowhere; this article introduces some thoughts on how to derive it from Word2Vec! Recently, my work has been closely related to pre-trained models, but I found that … Read more
Author丨Tian Yu Su Zhihu Column丨Machine Learning Link丨https://zhuanlan.zhihu.com/p/27234078 1. Introduction This sharing mainly focuses on the translation, understanding, and integration of two English documents on the Word2Vec model, both of which introduce the Skip-Gram model in Word2Vec. The next column article will implement the basic version of the Word2Vec Skip-Gram model using TensorFlow, so this article … Read more
Source | Zhihu Author | TianMin Link丨https://zhuanlan.zhihu.com/p/85998950 Editor | Deep Learning Matters WeChat Official Account This article is for academic exchange only. If there is any infringement, please contact for removal. [Introduction] Word2Vec is a widely used word embedding method. Due to recent research needs, I studied the algorithm model. Since there is a lot … Read more
Deep Learning Author: louwill From: Deep Learning Notes Language models are one of the core concepts in natural language processing. Word2Vec is a neural network-based language model and a method for word representation. Word2Vec includes two structures: skip-gram and CBOW (Continuous Bag of Words), but essentially both are operations for dimensionality reduction of vocabulary. Word2Vec … Read more