Discussing RNN Gradient Vanishing/Explosion Issues

Discussing RNN Gradient Vanishing/Explosion Issues

More Reading #Submission Guidelines# Get Your Paper Seen by More People How can we ensure that more quality content reaches readers quickly and reduces their search costs for high-quality content? The answer is: people you don’t know. There are always some people you don’t know who know what you want to know. PaperWeekly may serve … Read more

Introduction to Deep Learning Models: CNN and RNN

Introduction to Deep Learning Models: CNN and RNN

Author: Huang Yu, Autonomous Driving Scientist Editor: Hoh Xil Source: Huang Yu@Zhihu Produced by: DataFunTalk Note: There is a latest autonomous driving salon at the end of the article, welcome to sign up. Introduction: Deep learning has been “hot” for more than ten years since 2006, and the most common applications we see are in … Read more

Understanding RNN: A Beginner’s Guide to Machine Learning

Understanding RNN: A Beginner's Guide to Machine Learning

By Jiao Fa from Aofeisi Quantum Bit Report | WeChat Official Account QbitAI This tutorial is written for beginners in machine learning and will introduce what a Recurrent Neural Network (RNN) is. Recurrent Neural Networks are specifically designed to handle sequences. Due to their effectiveness in processing text, they are often used in Natural Language … Read more

How to Implement Image Semantic Segmentation Using CRF-RNN

How to Implement Image Semantic Segmentation Using CRF-RNN

Selected from GitHub Author: Shuai Zheng et al. Translated by: Machine Heart Contributors: Jiang Siyuan This GitHub project implements image semantic segmentation by combining CNN and CRF-RNN models. Readers can follow this project to implement the process using Keras/Tensorflow. Demo Address: http://crfasrnn.torr.vision Project Address: https://github.com/sadeepj/crfasrnn_keras This GitHub project contains methods to implement “CRF-RNN” image semantic … Read more

Discussing the Gradient Vanishing/Explosion Problem in RNNs

Discussing the Gradient Vanishing/Explosion Problem in RNNs

Follow the public account “ML_NLP“ Set as “Starred“, delivering heavyweight content to you first! Reprinted from | PaperWeekly ©PaperWeekly Original · Author|Su Jianlin Unit|Zhuiyi Technology Research Direction|NLP, Neural Networks Although Transformer models have conquered most fields in NLP, RNN models like LSTM and GRU still hold unique value in certain scenarios, making it worthwhile for … Read more

Exploring Parallel Computation in Non-Linear RNNs

Exploring Parallel Computation in Non-Linear RNNs

©PaperWeekly Original · Author | Su Jianlin Affiliation | Scientific Space Research Direction | NLP, Neural Networks In recent years, linear RNNs have attracted some researchers’ attention due to their characteristics such as parallel training and constant inference costs (for example, my previous article titled “Google’s New Work Attempts to ‘Revive’ RNN: Can RNN Shine … Read more

Understanding RNN: Recurrent Neural Networks and Their Implementation in PyTorch

Understanding RNN: Recurrent Neural Networks and Their Implementation in PyTorch

Click the “MLNLP” above to select the “Star” public account Heavyweight content delivered first-hand From | Zhihu Author | Lucas Address | https://zhuanlan.zhihu.com/p/85995376 Column | Deep Learning and Sentiment Analysis Editor | Machine Learning Algorithms and Natural Language Processing Understanding RNN: Recurrent Neural Networks and Their Implementation in PyTorch Recurrent Neural Networks (RNN) are a … Read more

Introduction to Recurrent Neural Networks (RNN): Vector to Sequence, Sequence to Sequence, Bidirectional RNN, Markovization

Introduction to Recurrent Neural Networks (RNN): Vector to Sequence, Sequence to Sequence, Bidirectional RNN, Markovization

Author: David 9 Address: http://nooverfit.com/ RNN seems to be better at information preservation and updating, while CNN seems to excel at precise feature extraction; RNN has flexible input and output dimensions, while CNN dimensions are relatively rigid. 1Question When talking about Recurrent Neural Networks, our first reaction might be: time sequence. Indeed, RNNs are good … Read more

Cracking Morse Code Using RNNs

Cracking Morse Code Using RNNs

Author | Sandeep Bhupatiraju Translator | Liu Zhiyong Editor | Debra Chen AI Frontline Overview: Over a century ago, in the United States, people used Morse code to send the first telegram in human history, opening a new chapter for mankind. The advent of Morse code has had a profound and far-reaching impact on human … Read more

A Detailed Explanation of RNN Stock Prediction (Python Code)

A Detailed Explanation of RNN Stock Prediction (Python Code)

Recurrent Neural Networks (RNN) are designed based on the recursive nature of sequential data (such as language, speech, and time series) and are a type of feedback neural network that contains loops and self-repetitions, hence the name “recurrent”. They are specifically used to handle sequential data, such as generating text word by word or predicting … Read more