Four Structures of RNN

Four Structures of RNN

Starting the Journey of RNN, Commonly Known Four Structures of RNN One to One: This is the traditional application of neural networks, usually used for simple input to output tasks. For example, in image classification, the network receives an image as input and identifies the category of the object represented in the image. Specifically, suppose … Read more

AI Image Recognition: Build Image Classification Models with Python

AI Image Recognition: Build Image Classification Models with Python

Hello everyone! Today I want to introduce an amazing AI tool—TensorFlow. How easy is it to build an image classification model with TensorFlow? Let me give you a few very practical examples: Scenario 1: Quickly Build a Simple Image Classification Model Suppose you have some images and you want the model to recognize which category … Read more

Essential Computer Vision Techniques: Classification, Localization, Detection, and Segmentation

Essential Computer Vision Techniques: Classification, Localization, Detection, and Segmentation

New Intelligence Column Author: Zhang Hao [New Intelligence Guide] The author of this article comes from the Machine Learning and Data Mining Institute (LAMDA) of the Computer Science Department of Nanjing University. This article systematically summarizes the applications of deep learning in four fundamental tasks in the field of computer vision, including image classification, localization, … Read more

Overview of Eight Major Tasks in Computer Vision

Overview of Eight Major Tasks in Computer Vision

This article is reprinted from the PaddlePaddle WeChat official account Editor’s Note: Written by a deep learning engineer from Baidu, this article provides a detailed overview of the eight major tasks in the field of computer vision, including: image classification, object detection, image semantic segmentation, scene text recognition, image generation, human keypoint detection, video classification, … Read more

ShuffleNetV2: The Crown Jewel of Lightweight CNNs

ShuffleNetV2: The Crown Jewel of Lightweight CNNs

Author: Ye Hu Editor: Tian Xu Introduction Recently, deep CNN networks like ResNet and DenseNet have significantly improved the accuracy of image classification. However, in addition to accuracy, computational complexity is also an important metric for CNN networks. Overly complex networks may be very slow, and specific scenarios, such as autonomous driving, require low latency. … Read more

Guide to Fooling Neural Networks: How to Trick Deep Learning Models

Guide to Fooling Neural Networks: How to Trick Deep Learning Models

This is a work by Big Data Digest, please refer to the end of the article for reproduction requirements. Original Author | Adam Geitgey Translation | Wu Shuang, Da Li, Da Jieqiong, Aileen To know oneself and one’s enemy, whether you want to become a hacker (which is not recommended!) or prevent future hacking intrusions, … Read more

Quick Start Guide to Building an Image Recognition System with TensorFlow

Quick Start Guide to Building an Image Recognition System with TensorFlow

Compiled by Li Lin, Produced by QbitAI | WeChat Official Account QbitAI From the various image recognition software we have seen, machines seem to recognize faces, cats, dogs, flowers, various cars, and other objects that appear in daily life. However, there is a prerequisite: you need to train the system with images of these categories. … Read more