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
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
Editor: We have a user who enjoys using machine learning to experiment with strategies. His descriptions of several models are quite vivid, and he has written a demo strategy using PonderLSTM, which we are sharing today~ The ACT model simulates the thinking process of complex problems by performing multiple computations at each time step (time … Read more
[Introduction]This article is a great technical blog written by Siavash Fahimi, mainly explaining how to implement Keras to realize RNN-LSTM for predicting the prices of Bitcoin and Ethereum. In the past year, besides AI, the hottest term in the internet industry has been blockchain. Although this article does not cover the technical explanation of blockchain, … 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
Click on the above “Beginner’s Guide to Vision” to choose to add “Star” or “Pin“ Important content delivered to you first Source | Zhihu Author | Lucas Address | https://zhuanlan.zhihu.com/p/88376673 Understanding Attention Mechanism and Its Implementation in PyTorch Biologically Inspired Attention Model -> Resource Allocation The attention mechanism in deep learning is a biomimetic of … Read more