Li Dakang1 minute ago 1. The purpose of this library is to explore methods for NLP text classification using deep learning. 2. It has various benchmark models for text classification. 3. It also supports multi-label classification, where multiple labels are associated with sentences or documents. Although many of these models are quite simple and may … Read more
Editor’s Note The real-time monitoring of tool wear during machining is of significant importance for reducing equipment downtime and lowering costs caused by tool wear. Traditional tool wear monitoring methods based on signal processing and shallow learning models require manual extraction of lengthy features, which cannot achieve intelligent monitoring. To overcome this inherent limitation, deep … 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
MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP graduate students, university professors, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
This article is reprinted with authorization from the WeChat public account “Robot Circle” (WeChat ID: ROBO_AI) The length of this article is 4473 words, and it is recommended to read it in 10 minutes This article introduces a library of NLP text classification deep learning methods and its 12 models. The purpose of this library … Read more
Source: New Intelligence This article contains 4263 words, suggested reading time is 8 minutes. This article discusses the recent paper published by 27 authors from DeepMind, Google Brain, MIT, and other institutions, proposing the “Graph Network” (GNN), which combines end-to-end learning with inductive reasoning, aiming to solve the problem of relational reasoning that deep learning … Read more
This article briefly introduces the main work of the paper accepted in April 2019 by Pattern Recognition titled “Recognizing Online Handwritten Chinese Characters Using RNNs with New Computing Architectures”. This paper mainly addresses the end-to-end recognition problem of handwritten Chinese characters. 1. Research Background Handwritten input is a very common human-computer interaction method. With the … Read more
01 Training Overview Python has become one of the most popular programming languages: according to the latest TIOBE rankings, Python has surpassed C# and is now among the top 4 most popular languages globally alongside Java, C, and C++. The simplicity, readability, and extensibility of Python, along with its numerous extension libraries, make it an … 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
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