Click on the “Datawhalee” above to select the “Starred“ public account Get valuable content at the first time Training techniques are very important for deep learning. As a highly experimental science, using different training methods on the same network structure can yield significantly different results. Here, I summarize my experiences from the past year and … Read more
Hello, I am Xiao Chu~ Today we are going to talk about a topic: using LSTM and KNN for multivariate data anomaly detection. Related Principles Multivariate Data Anomaly Detection is a very important task, especially in complex contexts such as high-dimensional data and time series data. Traditional anomaly detection methods (such as those based on … Read more
Big Data Digest Work Students studying the Stanford CS231n open course, take note! The detailed explanations for Assignment 1 – 3 are now available! Yesterday, Big Data Digest initiated a call for participants in the course by Andrew Ng and Li Fei-Fei, and the enthusiasm for the#SpringFestivalCheckIn# activity was exceptionally high! The Digest team has … Read more
In this blog, we will delve into the fascinating world of action recognition using the UCF101 dataset. Action recognition is a key task in computer vision, with applications ranging from surveillance to human-computer interaction. The UCF101 dataset serves as our playground for this exploration. Our goal is to build an action recognition model that combines … 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
Author: Occam’s Razor Personal Blog: https://blog.csdn.net/yilulvxing Paper Link: https://arxiv.org/abs/1704.02971 Github Code Link: https://github.com/LeronQ/DA-RNN The paper is titled “Recurrent Neural Network Based on Two-Stage Attention Mechanism”. Essentially, the article is based on the Seq2Seq model, combined with an attention mechanism to realize time series prediction methods. A major highlight of the article is that it introduces … 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
【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