Previously introduced were CNN (Convolutional Neural Network), BNN (Binarized Neural Network), dual-learning NMT and DBN, as well as deep learning optimization algorithms Batch Normalization and Layer Normalization. Students interested can add the WeChat public account “Deep Learning and NLP“, reply with keywords “CNN”, “BNN“, “dual”, “DBN“, BN and LN to get the corresponding article links. … Read more
Selected from KDnuggets Translated by Machine Heart Author:Michael Dietz Contributors: Jane W, Yan Qi, Wu Pan Generative Adversarial Networks (GANs) have gained significant attention in the research community recently. In this article, Michael Dietz, founder of Waya.ai, explains why GANs hold such potential and illustrates how GANs work through a vivid comparison with boxing matches. … Read more
Author丨Guo Xiaofeng Affiliation丨iQIYI Research Area丨Image Generation Recently, while studying GANs, I found that most of the current overview articles on GANs are from 2016 by Ian Goodfellow or from Professor Wang Feiyue of the Automation Institute. However, in the field of deep learning and GANs, progress is measured in months, and those two overviews feel … Read more
Introduction GAN, short for Generative Adversarial Networks, is a type of generative model. Personally, I like to call it the “involution” network. Why do I say this? Let’s start with a story!!! 01 The Story of Cops and Robbers On a distant planet in the universe, there is a city that is emerging, with various … Read more
1. Introduction Generative Adversarial Networks (GAN) is a deep learning model framework proposed by Ian Goodfellow and his team in 2014, first published in the paper “Generative Adversarial Networks”. Before the rise of deep learning, the main research directions for generative models included probabilistic graphical models (such as Hidden Markov Models (HMM)), variational inference methods … Read more
Source: https://en.wikipedia.org/wiki/Edmond_de_Belamy Five years ago, Generative Adversarial Networks (GANs) revolutionized the field of deep learning. This revolution led to significant technological breakthroughs. Ian Goodfellow and others proposed GANs in “Generative Adversarial Networks.” The academic and industrial sectors began to embrace and welcome the arrival of GANs. The rise of GANs was inevitable. Firstly, the most … Read more
Source: New Intelligence This article has 7372 words, recommended reading 10 minutes. This article organizes the speech content of Professor Tao Dacheng at the AI WORLD 2018 World Artificial Intelligence Summit on September 20. [ Introduction ] Professor Tao Dacheng from the University of Sydney, an academician of the Australian Academy of Science and chief … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered immediately! Source | PaperWeekly ©PaperWeekly Original · Author｜Sun Yudao School｜PhD student at Beijing University of Posts and Telecommunications Research Direction｜GAN Image Generation, Emotion Adversarial Sample Generation Thesis Title: A Mathematical Introduction to Generative Adversarial Nets Thesis Link: https://arxiv.org/abs/2009.00169 Introduction Since the groundbreaking work of … 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
Click the blue text to follow us Happy New Year, wishing you good fortune in the Year of the Dragon Happy Spring Festival LSTM Neural Network Time Series Prediction Given a simple dataset, we use Long Short-Term Memory (LSTM) neural networks to implement time series prediction, with the ReLU function as the activation function. The … Read more