Introduction: Deep learning can be applied in various fields, and the shapes of deep neural networks vary according to different application scenarios. The common deep learning models mainly include Fully Connected (FC), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Each of these has its own characteristics and plays an important role in different … Read more
Link: https://www.zhihu.com/question/59532432/answer/1510340606 Editor: Deep Learning and Computer Vision Disclaimer: For academic sharing only, please delete if infringed Time domain convolution = frequency domain multiplication. Most of the computations in convolutional neural networks occur in the convolution part. How to think about convolutional neural networks from the perspective of the frequency domain? How to explain ResNet … Read more
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
Selected from Medium Author: Eugenio Culurciello Translation by Machine Heart Contributors: Liu Xiaokun, Siyuan The author states: We have been trapped in the pit of RNNs, LSTMs, and their variants for many years; it is time to abandon them! In 2014, RNNs and LSTMs were revived. We all read Colah’s blog “Understanding LSTM Networks” and … Read more
The Transformer architecture has become a major component of the success of large language models (LLMs). To further improve LLMs, new architectures that may outperform the Transformer architecture are being developed. One such approach is Mamba, a state space model. The paper “Mamba: Linear-Time Sequence Modeling with Selective State Spaces” introduces Mamba, which we have … Read more
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
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