Selected from TowardsDataScience Author: Raimi Karim Contributors: Gao Xuan, Lu This article visually explains the attention mechanism with several animated diagrams and shares four NMT architectures that have emerged in the past five years, along with intuitive explanations of some concepts mentioned in the text. For decades, statistical machine translation has dominated translation models [9], … Read more
Machine Heart (Overseas) Original Author: Mos Zhang Participated by: Panda Machine Translation (MT) is the process of “automatically translating text from one natural language (source language) to another (target language)” using machines [1]. The idea of using machines for translation was first proposed by Warren Weaver in 1949. For a long time (from the 1950s … Read more
Selected from Google Research Authors: Quoc V. Le, Mike Schuster Translated by: Machine Heart Contributors: Wu Pan 2016 was a year of continuous breakthroughs in artificial intelligence. This year, we experienced breakthroughs in speech recognition, the flourishing of style transfer, advancements in neural machine translation, and more. Machine Heart closely followed each announcement. As the … Read more
Recently, an article titled “A Major Breakthrough in the Translation Field! As a Translator, I Now Understand the Concerns and Fears of 18th Century Textile Workers When They First Saw the Steam Engine!” has circulated among friends, causing many translators and foreign language students to express worries about the future of translation, suggesting that machine … Read more
Authorized Reprint from OReillyData Author | Raul Puri et al. How to Build and Train an Image Captioning Generator Using TensorFlow The image captioning model combines advances in computer vision and machine translation in recent years, using neural networks to generate captions for real images. For a given input image, the neural image captioning model … Read more
Machine Heart Column Author:Zhang Hao RNNs are very successful in handling sequential data. However, understanding RNNs and their variants, LSTM and GRU, remains a challenging task. This article introduces a simple and universal method for understanding LSTM and GRU. By simplifying the mathematical formalization of LSTM and GRU three times, we can visualize the data … Read more
Selected from arXiv Author:Vihar Kurama Translated by Machine Heart Contributors: Liu Xiaokun, Li Yazhou Recently, CMU and the University of Montreal proposed a novel multi-level memory RNN architecture—Nested LSTM. When accessing internal memory, Nested LSTM has greater flexibility compared to traditional Stacked LSTM, allowing it to handle longer temporal scales of internal memory; experiments also … Read more
【Introduction】Time series modeling is widely used in machine translation, speech recognition, and other related fields, making it an essential technology in the AI domain. This article will teach you how to build a Long Short-Term Memory network (LSTM) from scratch, using Bitcoin price prediction as an example. Author | Brian Mwangi Translated by | Zhuanzhi … Read more
Selected from echen Translated by Machine Heart Contributors: Machine Heart Editorial Team Long Short-Term Memory (LSTM) is a crucial neural network technology that has been widely applied in many fields, including speech recognition and natural language processing. In this article, Edwin Chen provides a systematic introduction to LSTM. Machine Heart has translated this article. The … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first-hand! From | Zhihu Author | Tian Yu Su https://www.zhihu.com/question/278825804/answer/402634502 Editor | Deep Learning This Small Matter Public Account This article is for academic exchange only. If there is any infringement, please contact the background for deletion. I have done some similar work, let … Read more