Artificial Neural Networks

BERT-of-Theseus: A Model Compression Method Based on Module Replacement

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BERT-of-Theseus: A Model Compression Method Based on Module Replacement

©PaperWeekly Original · Author｜Su Jianlin School｜Zhuiyi Technology Research Direction｜NLP, Neural Networks Recently, I learned about a BERT model compression method called “BERT-of-Theseus”, derived from the paper BERT-of-Theseus: Compressing BERT by Progressive Module Replacing. This is a model compression scheme built on the concept of “replaceability”. Compared to conventional methods like pruning and distillation, it appears … Read more

Introduction to Word Embeddings and Word2Vec

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Introduction to Word Embeddings and Word2Vec

Author: Dhruvil Karani Compiled by: ronghuaiyang Introduction This article introduces some basic concepts of word embeddings and Word2Vec. It is very straightforward and easy to understand. Word embeddings are one of the most common representations of a document’s vocabulary. They can capture the context, semantics, and syntactic similarities of a word in a document, as … Read more

An Analysis of word2vec Source Code

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An Analysis of word2vec Source Code

word2vec was launched by Google in 2013. The methods for obtaining word vectors, CBOW and Skip-gram models, are elaborated in the paper “Efficient Estimation of Word Representations in Vector Space.” The strategies for efficiently training models, Hierarchical Softmax and Negative Sampling, are discussed in “Distributed Representations of Words and Phrases and their Compositionality.” Since the … Read more

Understanding Word2vec Principles and Practice

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Understanding Word2vec Principles and Practice

Source: Submission Author: Aksy Editor: Senior Sister Video Link: https://ai.deepshare.net/detail/p_5ee62f90022ee_zFpnlHXA/6 5. Comparison of Models (Model Architectures Section of the Paper) Before the introduction of word2vec, NNLM and RNNLM trained word vectors by training language models using statistical methods. This section mainly compares the following three models: Feedforward Neural Net Language Model Recurrent Neural Net Language … Read more

In-Depth Analysis of Word2Vec Principles

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In-Depth Analysis of Word2Vec Principles

This Article Overview: 1. Background Knowledge Word2Vec is a type of language model that learns semantic knowledge from a large amount of text data in an unsupervised manner, and is widely used in natural language processing. Word2Vec is a tool for generating word vectors, and word vectors are closely related to language models. Therefore, we … Read more

Understanding Word2Vec: A Deep Dive into Word Embeddings

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Understanding Word2Vec: A Deep Dive into Word Embeddings

word2vec Word2Vec is a model used to generate word vectors. These models are shallow, two-layer neural networks trained to reconstruct linguistic word texts.The network represents words and needs to predict the input words in adjacent positions. In Word2Vec, under the bag-of-words model assumption, the order of words is not important. After training, the Word2Vec model … Read more

Illustrated Word2Vec: A Comprehensive Guide

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Illustrated Word2Vec: A Comprehensive Guide

Natural Language Processing Author: Machine Learning Beginner Original Author: Jalammar, Translated by Huang Haiguang Since 2013, word2vec has been an effective method for word embedding. This article presents word2vec in an illustrated manner, with no mathematical formulas, making it very easy to understand, and is recommended for beginners to read. (Original Author: jalammar, Translation: Huang … Read more

Reflecting on The Relationship Between Deep Learning and Traditional Computer Vision

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Reflecting on The Relationship Between Deep Learning and Traditional Computer Vision

▲Click the above Leifeng Network to follow To some extent, the greatest advantage of deep learning is its ability to automatically create features that no one would think of. Now, deep learning has a place in many fields, especially in computer vision. Although many people are fascinated by it, the deep network is essentially a … Read more

Li Fei-Fei’s Landmark Computer Vision Work: Stanford CS231n Assignment Detailed Explanation Part 3!

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Li Fei-Fei's Landmark Computer Vision Work: Stanford CS231n Assignment Detailed Explanation Part 3!

Big Data Digest Work Students studying the Stanford CS231n open course, take note! The detailed explanations for Assignment 1 – 3 are now available! Yesterday, Big Data Digest initiated a call for participants in the course by Andrew Ng and Li Fei-Fei, and the enthusiasm for the#SpringFestivalCheckIn# activity was exceptionally high! The Digest team has … Read more

Summary of PyTorch Loss Functions

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Summary of PyTorch Loss Functions

Source: Pythonic Biologist This article is about 1900 words long, and it is recommended to read it in 8 minutes. TensorFlow and PyTorch are quite similar; this article introduces loss functions using PyTorch as an example. 19 Types of Loss Functions 1. L1 Loss L1Loss Calculates the absolute difference between output and target. torch.nn.L1Loss(reduction='mean') Parameters: … Read more