Principles and Differences of CNN and RNN in Artificial Intelligence

Principles and Differences of CNN and RNN in Artificial Intelligence

Convolutional Neural Networks and Recurrent Neural Networks are widely used in machine learning today. However, they are typically used for completely different use cases. What are the principles and differences of CNN and RNN in artificial intelligence? In machine learning, each type of artificial neural network is tailored for specific tasks. Below, we will introduce … Read more

Understanding RNN Parameter Calculation

Understanding RNN Parameter Calculation

Regarding the calculation of RNN parameters, the PPT does not explain it very clearly, as it only contains images without text. At the same time, the textbook version by Qizhi Yao does not provide any exercises related to RNN parameter calculation, and the final exam may only test based on the descriptions in the images, … Read more

Manual for Recurrent Neural Networks (RNN)

Manual for Recurrent Neural Networks (RNN)

Recently, the Google Translate that has been spreading like wildfire among friends has achieved stunning performance. The core technology here is RNN – the so-called Recurrent Neural Network. RNN can be regarded as one of the most promising tools in deep learning’s future. Do you want to understand the source of its power? Do you … Read more

Exploring RNN Interpretability Methods Proposed by Zhou Zhihua et al.

Exploring RNN Interpretability Methods Proposed by Zhou Zhihua et al.

Selected from ArXiv Authors: Bo-Jian Hou, Zhi-Hua Zhou Contributors: Si Yuan, Xiao Kun This article is authorized for reproduction by Almost Human (almosthuman2014) Reproduction is prohibited Apart from numerical calculations, do you really know what neural networks are doing internally? We have always understood deep models based on their computational flow, but we are still … Read more

Can We Use RNNs to Write Strategies?

Can We Use RNNs to Write Strategies?

Editor: We have a user who enjoys using machine learning to experiment with strategies. His descriptions of several models are quite vivid, and he has written a demo strategy using PonderLSTM, which we are sharing today~ The ACT model simulates the thinking process of complex problems by performing multiple computations at each time step (time … Read more

Implementing a Neural Network from Scratch in Python

Implementing a Neural Network from Scratch in Python

Click the "Advanced Programming" above and select the "Star" public account Super valuable content delivered to you immediately!!! In this article, we will demonstrate how to build a simple three-layer neural network from scratch. Although we will not derive all the mathematical operations involved in detail, I will do my best to explain our approach … Read more

Tesla Executive Reveals Autonomous Driving Technology: 48 Neural Networks in Action, Detecting Up to 1000 Objects

Tesla Executive Reveals Autonomous Driving Technology: 48 Neural Networks in Action, Detecting Up to 1000 Objects

How Does Tesla Achieve City Autonomous Driving with Cameras? Written by | James Recently, Tesla updated its autonomous driving software to version 2020.12, which includes the automatic recognition of traffic lights and stop signs. If Tesla is equipped with the FSD full self-driving capability package, it can experience the autonomous driving feature of stopping at … Read more

An Overview of Graph Convolutional Networks

An Overview of Graph Convolutional Networks

Technical Column Author: Liu Zhongyu Edited by Luobotu Today, I want to share with you about Graph Convolutional Networks. With the development of artificial intelligence, many people have heard of concepts like machine learning, deep learning, and convolutional neural networks. However, Graph Convolutional Networks are not often mentioned. So, what are Graph Convolutional Networks? Simply … Read more

Optical Illusions: Blind Spots of Neural Networks

Optical Illusions: Blind Spots of Neural Networks

Click the image for details↑ Human vision is an extraordinary ability. Although it has evolved over millions of years in specific environments, it can accomplish tasks that early visual systems have never experienced. Reading is a great example, such as recognizing cars, airplanes, road signs, and other man-made objects. However, the visual system also has … Read more

A Survey of Graph Neural Networks (GNN)

A Survey of Graph Neural Networks (GNN)

Graph neural networks (GNNs) have gained widespread attention and are applied in scenarios such as recommendation systems, knowledge graphs, and traffic analysis due to their advantages in handling non-Euclidean space data and complex features. The irregularity of large-scale graph structures, the complexity of node features, and the dependency of training samples put immense pressure on … Read more