Convolutional Neural Networks

最新推荐文章于 2024-08-09 17:00:56 发布
原创 最新推荐文章于 2024-08-09 17:00:56 发布 · 383 阅读 · 1 ·
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本文旨在为初学者提供关于卷积神经网络(CNN)的全面介绍。内容涵盖CNN的基本概念、工作原理及其在自然语言处理(NLP)等领域的应用。通过通俗易懂的语言帮助读者快速掌握CNN的核心思想。

1. An Intuitive Explanation of Convolutional Neural Networks

2. Understanding Convolutional Neural Networks for NLP

3. Convolutional Neural Networks (CNNs / ConvNets)

4. Introduction to Deep Convolutional Neural Networks

5. The best explanation of Convolutional Neural Networks on the Internet!

6. A Friendly Introduction to Convolutional Neural Networks

7. Introduction to Convolutional Neural Networks (Stanford cs231a)

8. A Beginner's Guide To Understanding Convolutional Neural Networks

9. Convolutional Neural Network

10. Convolutional Neural Networks (LeNet)

Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
h__ang的博客
06-09 6427
论文名：Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition 中文名：脉冲卷积神经网络做高效的目标识别 摘要 深度神经网络（如CNN）已经在处理复杂的视觉问题（如目标识别）上展示了巨大的潜力。基于SNN的结构展示了使用基于脉冲的神经形态硬件实现低功耗的巨大潜力，这项工作描述了一种将CN...
Introduction to Convolutional Neural Network
闲敲棋子落灯花
09-30 1289
What’s the problem Full Connected layers to process image does not account the spatial structure of the images. Complicated images with multi-channels. When we try to improve our accuracy, we try to i
卷积神经网络简介(A introduction to convolutional neural networks )
xiaochaoqu的专栏
10-07 3176
最近一直在看卷积神经网络（CNN），把我对CNN的理解记录如下（主要是在computer vision 领域）。 本质上， 卷积神经网络是一种特殊的神经网络（NN）。CNN 与 NN 最大的不同在于结构的不同。 对于NN来说，层与层之间的神经元是完全连结的(fully connected),  这样会导致可调节参数过多，学习这些参数非常困难，容易出现过拟合的问题。另外一个问题这种完全连结的结构
cs231n'18：Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
FortiLZ的博客
06-26 511
一如既往，第一节课都是侃侃大山，没什么好说的。Lecture 1
Deep Learning for Computer Vision – Introduction to Convolution Neural Networks翻译
Stella_CYT的博客
03-02 952
原文链接：https://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/ 介绍： 人工智能的力量超出了我们的想象。我们都知道机器人在世界上一些强国已经进入了测试阶段。政府、大公司正在花费数十亿美元开发这种超智能生物。机器人的出现引起了世界上许多研究机构的关注。 它也让你兴奋吗？就我个人而言，学习机器人和人工智能的发展始于我内心深处的好奇和兴
ImageNet Classification with Deep Convolutional Neural Networks.pdf
03-15
标题和描述中提到的知识点是关于使用深度卷积神经网络（Deep Convolutional Neural Networks, DCNNs）进行ImageNet图像分类的研究。ImageNet是一个大规模的视觉识别挑战赛，旨在评估参赛模型的图像分类性能。在这项...
EfficientNet_Rethinking Model Scaling for Convolutional Neural Networks.pdf
05-20
此外，EfficientNet使用神经架构搜索（neural architecture search）来设计新的基线网络，并进一步规模化，以得到一系列的模型。这些模型被称为EfficientNets，并且在准确性和效率方面都超越了之前的卷积神经网络。...
Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting
02-08
Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting
(keras复现)Learning to Compare Image Patches via Convolutional Neural Networks.rar
01-13
Learning to Compare Image Patches via Convolutional Neural Networks论文的复现代码，这是keras版本，具体怎么做的可以看这篇文章https://blog.youkuaiyun.com/weixin_42521239/article/details/103389033
Single Image Dehazing via Multi-Scale Convolutional Neural Networks
04-05
在这篇题为“Single Image Dehazing via Multi-Scale Convolutional Neural Networks”的研究中，作者们提出了一个基于深度学习的框架，该框架使用卷积神经网络（CNN）来学习有效的特征以实现单图像去雾。...
Practical Convolutional Neural Networks Implement advanced d l models using Py
07-30
pdf+epub One stop guide to implementing award-winning, and cutting-edge CNN architectures Key FeaturesFast-paced guide with use cases and real-world examples to get well versed with CNN techniquesImplement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and moreImplement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention modelsBook Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learnFrom CNN basic building blocks to advanced concepts understand practical areas they can be applied toBuild an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize itLearn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracyUnderstand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and moreUnderstand the working of generative adversarial networks and how it can create new, unseen imagesWho This Book Is For This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected. Table of ContentsDeep Neural Networks - OverviewIntroduction to Convolutional Neural NetworksBuild Your First CNN and Performance OptimizationPopular CNN Model's ArchitecturesTransfer LearningAutoencoders for CNNObject Detection with CNNGenerative Adversarial NetworkVisual Attention Based CNN
Practical Convolutional Neural Networks
05-05
“Practical Convolutional Neural Networks: Implement advanced deep learning models using Python” Md. Rezaul Karim,Mohit Sewak,Pradeep Pujari 2018年2月 epub文件，内含示例源码
第三篇：An Introduction to Convolutional Neural Networks (C
AI天才研究院
09-03 2216
作者：禅与计算机程序设计艺术 1.简介 ： 近年来，卷积神经网络(Convolutional Neural Network，简称CNN)，正在成为深度学习领域里一个热门的话题。它的出现正逐渐成为图像、视频、语音等领域的主流技术，在图像识别、目标检测、跟踪、分类、文本生成等多个领域取得了成功。
【读论文】Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
qq_45664326的博客
01-23 613
IJCV 2015 Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition 论文总结
脉冲神经网络-Spiking Deep Convolutional Neural Network for Energy-Efficient Object Recognition
weixin_40936298的博客
02-20 1630
这篇论文是发表于2014年的一篇ANN-SNN的论文，也是非常早期的将ANN转为SNN的论文。本篇论文介绍了一种将深度卷积神经网络（CNN）转化为脉冲神经网络（SNN）的方法，以实现超低功耗的目标识别。通过调整CNN的架构以适应SNN的要求，并将学习到的网络权重应用于SNN架构，研究人员成功地实现了与原始CNN相似的目标识别准确率。
[Springer 2015] Spiking deep convolutional neural networks for energy-efficient object recognition
最新发布
qq_22841387的博客
08-09 605
深度学习神经网络，如卷积神经网络 (CNN) ，在诸如物体识别等复杂的视觉问题中表现出极大的潜力。而基于脉冲神经网络 (SNN) 的架构，则显示出在使用脉冲为基础的神经形态硬件上实现超低功耗的潜力。本文提出了一种新方法，将深度 CNN 转换为 SNN，从而使其可以在基于脉冲的硬件架构上运行。我们的方法首先对 CNN 架构进行调整，使其符合 SNN 的要求然后以常规方法训练调整后的 CNN最后将训练好的网络权重应用于基于该架构的 SNN。
[深度学习论文笔记] Convolutional Neuron Networks and its Applications
Hao_Zhang_Vision的博客
11-19 1424
In artificial intelligence, there exists a Moravec’s Paradox, 1 “High-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources”. It
（转）A Beginner's Guide To Understanding Convolutional Neural Networks Part 2
a1424262219的博客
08-25 242
Adit Deshpande CS Undergrad at UCLA ('19) BlogAbout A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In th...
convolutional neural networks
03-16
卷积神经网络（Convolutional Neural Networks，CNN）是一种深度学习网络架构，它使用卷积运算来提取图像中的特征。它通常用于图像分类、目标检测、图像生成等任务。CNN 由一系列卷积层、池化层、全连接层组成，其中卷积层和池化层负责特征提取，全连接层负责分类。
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