[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
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