Understanding LSTM for Mathematical Competitions

Understanding LSTM for Mathematical Competitions

Importance of Neural Networks in Mathematical Competitions In mathematical competitions, neural networks have gradually become a powerful assistant for participants due to their strong data processing and pattern recognition capabilities. Especially when dealing with complex, nonlinear, high-dimensional data, neural networks perform exceptionally well. They not only help us solve basic problems such as classification and … Read more

Step-By-Step Guide to Text Classification in Python

Step-By-Step Guide to Text Classification in Python

Author: Shivam Bansal Translated by: Shen Libin Proofread by: Ding Nanya This article is approximately 2300 words, and is recommended to be read in 8 minutes. This article will detail the text classification problem and implement this process in Python. Introduction Text classification is a common natural language processing task in business problems, with the … Read more

Machine Learning Challenges in 5G Communication Technology

Machine Learning Challenges in 5G Communication Technology

Wireless communication plays an important role in our daily lives. The latest 5G network communication aims to provide faster, more reliable, and more efficient data transmission speeds and services than the existing 4G networks. Meanwhile, the rapid development of artificial intelligence in recent years has provided new methods for solving problems in traditional communication technologies, … Read more

PredFormer: A Milestone in Spatial-Temporal Prediction Learning

PredFormer: A Milestone in Spatial-Temporal Prediction Learning

Follow our public account to discover the beauty of CV technology Spatial-temporal prediction learning is a field with a wide range of application scenarios, such as weather forecasting, traffic flow prediction, precipitation prediction, autonomous driving, and human motion prediction. When it comes to spatial-temporal prediction, we must mention the classic model ConvLSTM and the most … Read more

MSNovelist: A New Method for Generating Small Molecule Structures from Mass Spectra

MSNovelist: A New Method for Generating Small Molecule Structures from Mass Spectra

Compiled by | Jiang Changzhi Today, I would like to introduce an article published in Nature Methods by a team from ETH Zurich and Friedrich Schiller University Jena. The article proposes a new method for generating small molecule structures from mass spectrometry based on an encoder-decoder neural network: MSNovelist. It first uses SIRIUS and CSI: … Read more

Predicting Corrosion Rates Based on Recurrent Neural Networks

Predicting Corrosion Rates Based on Recurrent Neural Networks

In chemical plants, various process equipment are usually connected by pipelines. Therefore, pipelines are known as the “veins” of the chemical plant, serving the role of transporting various process media. The damage to pipelines during operation is often caused by corrosion. According to statistics, the losses caused by metal corrosion in China exceed 40 billion … Read more

Understanding Deep Learning: A Comprehensive Guide

Understanding Deep Learning: A Comprehensive Guide

Figure1. Deep Learning Mind Map Introduction The concept of deep learning can be traced back to the field of cybernetics between 1940 and 1960. It later developed into connectionism during the 1980s and 1990s, with the third wave of development beginning in 2006 with the expansion of artificial neural networks, evolving into the highly popular … Read more

Applications and Impacts of Large Model Technology in Autonomous Driving

Applications and Impacts of Large Model Technology in Autonomous Driving

This article first summarizes the development history of large model technology, the iterative path of autonomous driving models, and the role of large models in the autonomous driving industry. Next, it details the basic definition, fundamental functions, and key technologies of large models, especially the Transformer attention mechanism and the pre-training-fine-tuning paradigm. The article also … Read more

Thorough Understanding of RNN (Recurrent Neural Networks)

Thorough Understanding of RNN (Recurrent Neural Networks)

This article is a bit long, and I will divide it into several parts. Through this article, I hope to help you thoroughly understand the principles of RNN (Recurrent Neural Networks) and be able to implement it at the code level. Table of Contents What This Article Does Inputs and Outputs of RNN RNN Network … Read more

Differences Between CNN and RNN in Deep Learning

Differences Between CNN and RNN in Deep Learning

CNN and RNN are the two most commonly used deep learning network structures in deep learning, and some students may still be unclear about the differences between these two networks. Now let’s illustrate the specific applications of CNN and RNN with a diagram: One to One: This represents the scenario of the CNN network, from … Read more