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
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
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
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
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
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
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
Originally from Python AI Frontiers This article systematically and comprehensively organizes the introduction and algorithm principles of various deep learning models. 1 Main Text Deep learning methods utilize neural network models for advanced pattern recognition and automatic feature extraction, achieving significant results in the field of data mining in recent years. Common models include not … Read more
Source: Machine Learning Algorithms Explained 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, … Read more
This article is approximately 3900 words long and is recommended for an 8-minute read. In this article, we demonstrate that Causal Attention can be rewritten in the form of an RNN. In recent years, RNNs have rekindled interest among researchers and users due to their linear training and inference efficiency, hinting at a “Renaissance” in … Read more