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
Selected from Reddit Author: Ian Williamson Translated by Machine Heart Contributors: Wang Zhi Jia, Mo Wang A study from Stanford University found a correspondence between waves in physics and computations in RNNs. Paper link:https://advances.sciencemag.org/content/5/12/eaay6946 GitHub link:https://github.com/fancompute/wavetorch Recently, there has been a lot of exciting interaction between machine learning and some fields of physics and numerical … 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
【Introduction】This article provides the background and overview of Dropout, as well as a parameter analysis of its application in language modeling using LSTM / GRU recurrent neural networks. Author｜Adrian G Compiler｜Zhuanzhi (No secondary reproduction), Xiaoshi Organizer｜Yingying Dropout Inspired by the role of gender in evolution, Hinton et al. first proposed Dropout, which temporarily removes units … 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
Introduction I rarely write summary articles, but I feel it’s necessary to periodically summarize some interconnected knowledge points, so I’ve written this one. Since my content mainly focuses on time series and spatio-temporal prediction, I will primarily discuss RNN, LSTM, GRU, ConvLSTM, ConvGRU, and ST-LSTM. 1. RNN The most primitive recurrent neural network, essentially a … Read more
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