Click on the above “Beginner’s Guide to Visual Learning” to choose to add “Star” or “Top” Heavyweight content delivered at the first time Editor | Anke Produced by | Panchuang AI Technology Team Table of Contents: Introduction to Gated Recurrent Neural Networks Long Short-Term Memory Networks (LSTM) Gated Recurrent Units (GRU) Implementing LSTM and GRU … Read more
Click on the above “Visual Learning for Beginners“, select to add to favorites or pin. Important content delivered in real-time 1. What is LSTM LSTM stands for Long Short-Term Memory, a type of recurrent neural network (RNN) that can handle sequential data and is widely used in fields such as natural language processing and speech … Read more
Originally published by Data Practitioners The process of neural networks includes the updating of its weight matrix (Weight Matrix: WM). Once the network training is complete, the weight matrix will be permanently fixed, and its effectiveness is evaluated based on the network’s generalization results on the test data. However, many environments continue to evolve after … Read more
Long Short-Term Memory networks (LSTM), as an improved version of Recurrent Neural Networks (RNN), not only solve the problem of RNNs being unable to handle long-distance dependencies but also address common issues in neural networks such as gradient explosion or gradient vanishing, making them very effective in processing sequential data. What are the fundamental reasons … Read more
Written by丨Zhang Tianrong He is not the first person to endow neural networks with “memory,” but the long short-term memory network (LSTM) he invented has provided neural networks with longer and practically useful memory. LSTM has long been used by Google, Apple, Amazon, Facebook, etc., to implement functions such as speech recognition and translation. Today, … Read more
Author on Zhihu | master苏 Link | https://zhuanlan.zhihu.com/p/139617364 This article is approximately 3200 words, recommended reading 5 minutes This article introduces the visualization of the structure of LSTM models. I have recently been learning about the application of LSTM in time series prediction, but I encountered a significant issue: the structure of LSTM becomes very … Read more
Abstract Artificial Intelligence (AI) has entered a new period of vigorous development. The driving forces behind this wave of AI are three major engines: Deep Learning (DL), Big Data, and Large-Scale Parallel Computing, with DL at the core. This article reviews the basic situation of the “revival of deep neural networks,” briefly introduces four commonly … Read more
This article will introduce the use of a simple RNN model for time series prediction. For example, we currently have a segment of a sine curve as shown in the figure below. We will input the red part and train the model to output the next segment’s values. First, let’s analyze it. Assuming we input … Read more
In the previous article, we summarized RNNs (Recurrent Neural Networks). Due to the gradient vanishing problem in RNNs, it is challenging to handle long sequences of data. Experts have improved RNNs, resulting in a special case called LSTM (Long Short-Term Memory), which can avoid the gradient vanishing problem typical of conventional RNNs, thus gaining widespread … Read more
Author: Jaime Zornoza, Technical University of Madrid Translation: Chen Zhiyan Proofreading: Wang Weili This article is approximately 3700 words, and it is recommended to read in 10+ minutes. This article will help you understand deep learning neural networks in a way never seen before, and build a Chatbot using NLP! Have you ever fantasized about … Read more