Click on the “AI有道” above to select the “Star” public account Heavyweight content delivered first-hand This article is adapted from DataWhale Training techniques are very important for deep learning. As a highly experimental science, the same network structure trained with different methods can yield significantly different results. Here, I summarize my experiences over the past … Read more
From | DataWhale Training techniques are very important for deep learning. As a highly experimental science, even the same network architecture trained with different methods can yield significantly different results. Here, I summarize my experiences from the past year and share them with everyone. I also welcome additions and corrections. Parameter Initialization Any of the … Read more
Author: Zhang Jianzhong Source: http://blog.csdn.net/zouxy09/article/details/8775518 Deep learning is a new field in the study of machine learning, motivated by the establishment and simulation of neural networks that analyze and learn like the human brain. It mimics the mechanisms of the human brain to interpret data such as images, sounds, and text. Deep learning is a … Read more
Selected from Medium Author: Arvind N Translated by Machine Heart Contributors: Lu Xue, Li Zenan On August 8, Andrew Ng officially launched Deeplearning.ai—a series of deep learning courses based on Coursera, aiming to spread foundational knowledge of artificial intelligence to more people. A week later, many have completed the first three courses that are currently … Read more
Introduction The concept of deep learning originates from the study of artificial neural networks. A multi-layer perceptron with multiple hidden layers is a type of deep learning structure. Deep learning combines low-level features to form more abstract high-level representations of attribute categories or features, in order to discover distributed feature representations of data. The concept … Read more
©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
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
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
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
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