Star★TopPublic Account Love You All♥ Editor: 1+1=6 Introduction Many econophysicists have noted that portfolios constructed using the empirical correlation matrix estimated from stock (or other asset) returns are very similar to those obtained from minimum variance optimization using the empirical covariance estimated for the same stocks. The companies of the minimum risk Markowitz portfolio [MVP] … Read more
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
In the previous article, someone raised the question in a comment about whether generative adversarial networks could be used to simulate shooting covers. This was previously tested in a simple experiment, but the results were not ideal. The unsatisfactory results stem from the implementation of general generative adversarial networks, which essentially involves a transformation or … 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
Author: Mirko Translated by: Sour Bun Wishing you a peaceful Dragon Boat Festival Generative Adversarial Network Applications in Quantitative Investing Series (Part 1) Get the complete code at the end of the article 1 Introduction Overfitting is one of the challenges we face when applying machine learning techniques to time series. This issue arises because … 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
The powerful feature representation and nonlinear fitting capability of deep neural networks stem from sufficient learning on high-quality datasets. However, in practical engineering applications, due to economic and labor costs, acquiring a large amount of typical labeled data becomes extremely challenging, resulting in avery limited number of training samples. Data augmentation methods provide a simple … Read more
From | CSDN Blog Author | JensLee Edited | Deep Learning This Little Thing Public Account This article is for academic exchange only. If there is any infringement, please contact the backend to delete it. Understanding GAN networks (Generative Adversarial Networks) from a beginner’s perspective can be thought of as a forgery machine, creating things … Read more
Recommended by New Intelligence Source:Special Knowledge (LiteProgrammer) 【New Intelligence Introduction】InterSpeech is the top conference in the field of speech processing, held from September 15 to 20 in Graz, Austria. Professor Li Hongyi from National Taiwan University presented a report titled “Generative Adversarial Network and its Application to Speech Processing and Natural Language Processing”. This article … Read more
