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
For More Content, Please Follow: Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 1) Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2) 23-24 Basics of Recurrent Neural Networks As shown above, sequence problems such as named entity recognition account for a significant proportion of real-life applications, while traditional machine learning … Read more
Follow the official account “ML_NLP“ Set as “Starred“, essential resources delivered first-hand! Looking back at the phrase from 2017, “Attention is all you need”, it truly was a prophetic statement. The Transformer model started with machine translation in natural language processing, gradually influencing the field (I was still using LSTM in my graduation thesis in … Read more
Follow us on WeChat “ML_NLP” Set as “Starred”, delivering valuable content to you first! Produced by Machine Learning Algorithms and Natural Language Processing Original Column Author on WeChat @ Yi Zhen School | PhD Student at Harbin Institute of Technology SCIR 1. Why RNN Needs to Handle Variable Length Inputs Assuming we have an example … Read more
Case Introduction This case will demonstrate how to use Recurrent Neural Networks (RNNs) for time series prediction. Specifically, we will use RNNs to predict the future values of a variable that depends on its own historical values. In this case, we will use a temperature dataset. We will provide the temperature values from the past … 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
Sharing a paper on video super-resolution titled Revisiting Temporal Modeling for Video Super-resolution, which is a BMVC 2020 paper. The results of this paper currently rank first on several datasets for video super-resolution, and the code has been open-sourced. Affiliations: Tsinghua University, New York University, Huawei Noah’s Ark Lab 1 Highlights This paper proposes a … Read more
This article is approximately 2600 words long and is recommended to be read in 9 minutes. The SUPRA method significantly improves model stability and performance by replacing softmax normalization with GroupNorm. Transformers have established themselves as the primary model architecture, particularly due to their outstanding performance across various tasks. However, the memory-intensive nature of Transformers … Read more
Friends familiar with deep learning know that LSTM is a type of RNN model that can conveniently handle time series data and is widely used in fields such as NLP. After watching Professor Li Hongyi’s deep learning videos from National Taiwan University, especially the first part introducing RNN and LSTM, I felt enlightened. This article … Read more
Recently, the intersection of physics, mathematics, and machine learning has promoted the use of machine learning frameworks to optimize physical models, further encouraging researchers to develop many exciting new machine learning models (such as Neural ODEs, Hamiltonian Neural Networks, etc.) that draw on concepts from physics. Researchers from Stanford University’s Shanhui Fan group are particularly … Read more