Understanding Convolutional Neural Networks (CNN) Convolutional Neural Networks (CNN) are a type of feedforward neural network where artificial neurons can respond to a portion of the surrounding units within a coverage area, demonstrating outstanding performance in large image processing. CNN has five characteristics: 1. Local perception; 2. Parameter sharing; 3. Sampling; 4. Multiple convolutional kernels; … Read more
I am a master’s student at a double first-class university, and I am currently preparing for the 2024 autumn recruitment. While looking for internships in large model algorithm positions, I encountered many interesting interviews, so I decided to record these interview questions and share them with friends who, like me, are striving for a satisfactory … Read more
This article is a translated version of the notes from Stanford University’s CS224d course, authorized by Professor Richard Socher of Stanford University. Unauthorized reproduction is prohibited; for specific reproduction requirements, please see the end of the article. Translation: Hu Yang & Xu Ke Proofreading: Han Xiaoyang & Long Xincheng Editor’s Note: This article is the … Read more
Source: Panchuang AI, Author: VK Panchuang AI Share Author | Renu Khandelwal Compiler | VK Source | Medium We start with the following questions: Recurrent Neural Networks can solve the problems present in Artificial Neural Networks and Convolutional Neural Networks. Where can RNNs be used? What is RNN and how does it work? Challenges of … Read more
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
RNN is a popular model for processing time series data, demonstrating significant effectiveness in fields such as NLP and time series forecasting.As this article focuses on the practical application of RNN rather than theoretical knowledge, interested readers are encouraged to study RNN systematically. The following example is implemented using TensorFlow.Using TensorFlow to implement RNN or … Read more
Neural networks are the backbone of deep learning, and among the various neural network models, RNNs are the most classic. Despite their imperfections, they possess the ability to learn from historical information. Subsequent frameworks, whether the encode-decode framework, attention models, self-attention models, or the more powerful Bert model family, have evolved and strengthened by standing … Read more
Machine Heart Report Editor: Egg Sauce AI writing AI came faster than expected. Since its launch at the end of last year, the conversational AI model ChatGPT has become popular throughout the community. ChatGPT is indeed an amazing tool, like a “Pandora’s box.” Once you find the right way to open it, you may find … Read more
(Click the public account above, you can quickly follow) Source: Poll’s Notes cnblogs.com/maybe2030/p/5597716.html If you have good articles to submit, please click → here for details Table of Contents 1. Neuron Model 2. Perceptron and Neural Networks 3. Backpropagation Algorithm 4. Common Neural Network Models 5. Deep Learning 6. References Currently, deep learning (Deep Learning, … Read more
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