The Era of Transformers: Why LSTM Models Still Matter

The Era of Transformers: Why LSTM Models Still Matter

Follow us on WeChat “ML_NLP” Set as “Starred” for heavy content delivered first-hand! Source | Zhihu Author | DengBoCong Link | https://www.zhihu.com/question/439243827/answer/1685085848 Editor | Machine Learning Algorithms and Natural Language Processing WeChat Account This article is for academic sharing. If there is an infringement, please contact us to delete it. I wrote a lot specifically … Read more

Understanding Long Short-Term Memory Networks (LSTM)

Understanding Long Short-Term Memory Networks (LSTM)

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

Understanding Transformer Principles and Their Applications in CV

Understanding Transformer Principles and Their Applications in CV

Currently, there are applications based on Transformer in three major image problems:Classification (ViT), Detection (DETR) and Segmentation (SETR), all achieving good results. In the future, could Transformer possibly replace CNN? Will Transformer revolutionize the CV field just like its application in NLP? What might the research directions be? Please look forward to the next article … Read more

Deep Learning Methods for NLP Text Classification

Deep Learning Methods for NLP Text Classification

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

NLP Text Classification Deep Learning Methods Library

NLP Text Classification Deep Learning Methods Library

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

Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2)

Andrew Ng: Deep Learning Knowledge Explained in 28 Images (Part 2)

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

How to Create RNN Directly from NumPy

How to Create RNN Directly from NumPy

Click on the above“Beginner’s Guide to Vision” to choose to add Star or Pin. Important content delivered promptly Mu Yi from A Fei Temple From | Quantum Bits Using mature frameworks like Tensorflow and PyTorch to implement Recurrent Neural Networks (RNNs) has greatly lowered the technical barriers to entry. However, for beginners, this is still … Read more

Understanding RNN (Recurrent Neural Networks) Basics

Understanding RNN (Recurrent Neural Networks) Basics

Click the “MLNLP” above and select “Star” for the public account Heavy content delivered first-hand Author | Yi Zhen Address | https://zhuanlan.zhihu.com/p/30844905 Column | Machine Learning Algorithms and Natural Language Processing Understanding RNN (Recurrent Neural Networks) Basics Basics of Neural Networks Neural networks can be considered as black boxes that can fit any function. As … Read more

The Last Mile of Large Models: A Comprehensive Review of Large Model Evaluation

The Last Mile of Large Models: A Comprehensive Review of Large Model Evaluation

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and researchers from enterprises. The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. … Read more

Ten Research Directions Worth Exploring in Large Models

Ten Research Directions Worth Exploring in Large Models

Source: OpenBMB Open Source Community This article is 14,629 words long and is recommended to be read in 20 minutes. This article introduces Professor Liu Zhiyuan, an associate professor in the Department of Computer Science at Tsinghua University and one of the main initiators of OpenBMB, answering the question on Zhihu, "What are the academic … Read more