【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
Selected from ArXiv Authors: Bo-Jian Hou, Zhi-Hua Zhou Contributors: Si Yuan, Xiao Kun This article is authorized for reproduction by Almost Human (almosthuman2014) Reproduction is prohibited Apart from numerical calculations, do you really know what neural networks are doing internally? We have always understood deep models based on their computational flow, but we are still … 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
In the previous lecture, we discussed the seq2seq model. Although the seq2seq model is powerful, its effectiveness can be significantly reduced if used in isolation. This section introduces the attention model, which simulates the human attention intuition within the encoder-decoder framework. Principle of Attention Mechanism The attention mechanism in the human brain is essentially a … Read more
The common challenge faced by non-Transformer architectures is still to prove how high their ceiling can be. Author: Zhang Jin Editor: Chen Caixian The paper “Attention Is All You Need” published by Google in 2017 has become a bible for artificial intelligence today, and the global AI boom can be directly traced back to the … Read more