Produced by Big Data Digest Compiled by: Xiao Qi, Mixed Sweet, Xia Yawei Trump’s new re-election campaign has begun. The author’s interest in Trump’s distinctive language style raises the question: can a speech that embodies his style be generated using a Recurrent Neural Network (RNN) trained on his tweets and speeches? The conclusion is that … Read more
Produced by Big Data Digest Compiled by: Andy The backpropagation algorithm belongs to deep learning and plays an important role in solving model optimization problems. This algorithm was proposed by Geoffrey Hinton, known as the father of deep learning. In 1986, he published a paper titled “Learning representations by back-propagating errors” (Rumelhart, Hinton & Williams, … Read more
Produced by Big Data Digest Authors:Ning Jing, Wei Zimin Have you ever thought about moving neural networks from computers into a piece of glass? Using neural networks for image recognition and intelligent recommendations has become very common. In recent years, the increase in computing power and parallel processing has made it a very practical technology. … Read more
Soviet mathematician Andrey N. Kolmogorov (1903-1987). Image source: https://wolffund.org.il/ Introduction: Large models pose new questions to computational theory, which can also assist large models in revealing first principles, thereby finding boundaries and directions. For example, the KA Superposition Theorem completed by Soviet mathematician Kolmogorov and his student Arnold in the 1950s. Nick | Author Xiaoxue … Read more
-Distilling the Knowledge in a Neural Network Geoffrey Hinton∗†Google Inc. Mountain View [email protected] Oriol Vinyals† Google Inc. Mountain View [email protected] Jeff Dean Google Inc. [email protected] Abstract A simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then average their predictions.[3] Unfortunately, … Read more
In the field of deep learning, there are many specialized terms that can be quite overwhelming at first glance. However, as we delve deeper, we gradually start to understand them, albeit feeling like something is still missing. Today, we will discuss a specialized term called Attention mechanism! 1. Intuitive Understanding of Attention Imagine a scenario … Read more
Produced by Machine Learning Algorithms and Natural Language Processing @Official Account Original Column Author Don.hub Position | Algorithm Engineer at JD.com School | Imperial College London Outline Intuition Analysis Pros Cons From Seq2Seq To Attention Model Seq2Seq is important, but its flaws are obvious Attention was born Write the encoder and decoder model Taxonomy of … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The community’s vision is to promote communication and progress among the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
On January 24, at the “New Architecture, New Model Power” large model launch conference held by Shanghai Yanxin Intelligent AI Technology Co., Ltd., Yanxin officially released the first general-purpose natural language large model in China that does not use the Attention mechanism—Yan model. As one of the few non-Transformer large models in the industry, the … Read more
The attention mechanism is mentioned in both of the following articles: How to make chatbot conversations more informative and how to automatically generate text summaries. Today, let’s take a look at what attention is. This paper is considered the first work using the attention mechanism in NLP. They applied the attention mechanism to Neural Machine … Read more