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Join the professional CV group of Jishi, interact with 6000+ visual developers from top companies and universities such as Tencent, Huawei, Baidu, Peking University, Tsinghua University, and the Chinese Academy of Sciences! There is also an opportunity to interact with Professor Kai-Fu Lee and other experts! We also provide monthly expert live sharing, real project … Read more
【Introduction】The traditional visual recognition methods struggle to directly distinguish between NIs (Natural Images) and CG (Computer Generated images). This article proposes an efficient image recognition method based on Convolutional Neural Networks (CNNs). A large number of experiments were conducted to evaluate the model’s performance. The experimental results show that this method outperforms existing recognition methods … Read more
Click on the above “AI Youdao“, select “Star” public account Heavyweight content delivered first time Author | Raimi Karim Translator | Major Editor | Zhao Xue Produced by | AI Technology Camp (ID: rgznai100) Introduction: In recent years, many Convolutional Neural Networks (CNN) have come into the spotlight, and as their depths become increasingly profound, … Read more
100 Questions on Deep Learning Author: louwill Machine Learning Lab The MobileNet series, as a representative of lightweight networks, makes the lightweight deployment of CNNs on mobile devices possible. Currently, there are three versions of MobileNet: MobileNet v1, MobileNet v2, and MobileNet v3. This article focuses on elaborating the MobileNet series networks, which is essential … Read more
Click Machine Learning Algorithms and Python Learning ，Select Star Don’t miss out on the wonderful content Author | Raimi Karim, Produced by | AI Technology Camp (ID: rgznai100) This article carefully selects 10 detailed diagrams of CNN architectures for discussion. Curated by the author. These diagrams showcase the essence of the entire model without the … Read more
01 Introduction This article reviews some of the more ingeniously designed and practical “plugins” used in CNN networks. The so-called “plugins” are modules that do not alter the main structure of the network and can be easily integrated into mainstream networks to enhance their feature extraction capabilities, achieving a plug-and-play functionality. Many similar reviews claim … Read more
Selected from offconvex Author：John Miller Translated by Machine Heart Contributors: Qianshu, Zhang Qian, Siyuan In recent years, while Recurrent Neural Networks (RNNs) have been dominant, models like autoregressive Wavenet or Transformers are now replacing RNNs in various sequence modeling tasks. Machine Heart has previously introduced RNNs and CNNs for sequence modeling in a GitHub project, … Read more
Selected from MachineLearningMastery Author: Jason Brownlee Translated by Machine Heart Contributors: Nurhachu Null, Lu Xue The encoder-decoder structure has shown advanced levels in several fields, but this structure encodes the input sequence into a fixed-length internal representation. This limits the length of the input sequence and results in poorer performance of the model on particularly … Read more
RNNs, or Recurrent Neural Networks, are an important theoretical tool in both machine learning and computational neuroscience. In today’s world dominated by transformers, many may have forgotten about RNNs. However, RNNs remain a fundamental type of neural network and will surely play a role in the era of large models. First, let’s look at the … Read more
Introduction Today I would like to share a Perspective paper published in October 2023 in Nature Review Neuroscience by Daniel Durstewitz, Georgia Koppe, and Max Ingo Thurm from Heidelberg University, Germany. The title of the paper is Reconstructing computational system dynamics from neural data with recurrent neural networks. This article focuses on data-driven reconstruction of … Read more