Follow our public account to discover the beauty of CV technology This article is reprinted from Machine Heart. The key to the tremendous success of the Transformer in deep learning is the attention mechanism. The attention mechanism allows Transformer-based models to focus on parts of the input sequence that are relevant, achieving better contextual understanding. … Read more
MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and researchers from enterprises. The Community’s Vision is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, especially … Read more
Original link: https://arxiv.org/pdf/1909.06317.pdf Abstract Sequence-to-sequence models are widely used in end-to-end speech processing, such as Automatic Speech Recognition (ASR), Speech Translation (ST), and Text-to-Speech (TTS). This paper focuses on a novel sequence-to-sequence model called the Transformer, which has achieved state-of-the-art performance in neural machine translation and other natural language processing applications. We conducted an in-depth … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering graduate students, faculty, and researchers in NLP. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | AI Technology Review … Read more
Source: Algorithm Advancement This article is about 4000 words long and is recommended for an 8-minute read. This article deeply explores the potential connections between Transformer, Recurrent Neural Networks (RNN), and State Space Models (SSM). By exploring the potential connections between seemingly unrelated Large Language Model (LLM) architectures, we may open up new avenues for … 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
Click on the “Datawhalee” above to select the “Starred“ public account Get valuable content at the first time Training techniques are very important for deep learning. As a highly experimental science, using different training methods on the same network structure can yield significantly different results. Here, I summarize my experiences from the past year and … Read more
Introduction: Deep learning can be applied in various fields, and the shapes of deep neural networks vary according to different application scenarios. The common deep learning models mainly include Fully Connected (FC), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Each of these has its own characteristics and plays an important role in different … Read more
This article is a translated version of the notes from Stanford University’s CS224d course, authorized by Professor Richard Socher of Stanford University. Unauthorized reproduction is prohibited; for specific reproduction requirements, please see the end of the article. Translation: Hu Yang & Xu Ke Proofreading: Han Xiaoyang & Long Xincheng Editor’s Note: This article is the … Read more
New Intelligence Report Source: Reddit, Science Editors: Daming, Pengfei [New Intelligence Guide]Recently, Stanford University Chinese Professor Shanhui Fan’s team published an article in a sub-journal of Science, pointing out that whether it is sound waves, light waves, or other forms of waves, their descriptive equations can be equivalent to Recurrent Neural Networks (RNNs). This discovery … Read more