Selected from ArXiv Authors: Bo-Jian Hou, Zhi-Hua Zhou Contributors: Si Yuan, Xiao Kun This article is authorized for reproduction by Almost Human (almosthuman2014) Reproduction is prohibited Apart from numerical calculations, do you really know what neural networks are doing internally? We have always understood deep models based on their computational flow, but we are still … Read more
Editor: We have a user who enjoys using machine learning to experiment with strategies. His descriptions of several models are quite vivid, and he has written a demo strategy using PonderLSTM, which we are sharing today~ The ACT model simulates the thinking process of complex problems by performing multiple computations at each time step (time … Read more
[Introduction]This article is a great technical blog written by Siavash Fahimi, mainly explaining how to implement Keras to realize RNN-LSTM for predicting the prices of Bitcoin and Ethereum. In the past year, besides AI, the hottest term in the internet industry has been blockchain. Although this article does not cover the technical explanation of blockchain, … Read more
Neural networks are the backbone of deep learning, and among the various neural network models, RNNs are the most classic. Despite their imperfections, they possess the ability to learn from historical information. Subsequent frameworks, whether the encode-decode framework, attention models, self-attention models, or the more powerful Bert model family, have evolved and strengthened by standing … Read more
In the previous lecture, we discussed the seq2seq model. Although the seq2seq model is powerful, its effectiveness can be significantly reduced if used in isolation. This section introduces the attention model, which simulates the human attention intuition within the encoder-decoder framework. Principle of Attention Mechanism The attention mechanism in the human brain is essentially a … Read more
The common challenge faced by non-Transformer architectures is still to prove how high their ceiling can be. Author: Zhang Jin Editor: Chen Caixian The paper “Attention Is All You Need” published by Google in 2017 has become a bible for artificial intelligence today, and the global AI boom can be directly traced back to the … Read more
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