MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience that includes NLP master’s and PhD students, university professors, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for … Read more
Click on the above “CVer“, select to add “Star” or “Top” Important content delivered instantly Author: zzq https://zhuanlan.zhihu.com/p/68411179 This article is authorized, and reprinting without permission is prohibited Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between distant pixels are … Read more
From | Zhihu Author | zzq Address | https://zhuanlan.zhihu.com/p/68411179 This article is for communication purposes only. If there is any infringement, please contact for deletion. Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between distant pixels are relatively weak. Therefore, … Read more
Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between pixels that are far apart are weaker. Therefore, each neuron does not need to perceive the entire image globally; it only needs to perceive local information, which can then be combined … Read more
Convolutional Neural Networks (CNN) are a type of feedforward neural network that includes convolutional computations and has a deep structure. They are one of the representative algorithms of deep learning. This article aims to introduce the basic concepts and structures of CNN, as well as the fundamental ideas behind CNN architecture design. This article is … 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
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
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
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
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