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Why the Original Transformer Figure Is Wrong, and Some Other Interesting Historical Tidbits About LLMs

A few months ago, I shared the article, Understanding Large Language Models: A Cross-Section of the Most Relevant Literature To Get Up to Speed, and the positive feedback was very motivating! So, I also added a few papers here and there to keep the list fresh and relevant.
Juergen Schmidhuber Substack Notesor Dynamic Recurrent Neural Networks Understanding Large Language Models Layer Normalization Transformer Architecture

"Text classification based on machine learning for Tibetan social netwo" by Hui Lv, Fenfang Li et al.

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning mo...
Tibetan Social Network Convolutional Neural Networks Naive Bayesian Random Forest Support Vector Machine Tibetan Social
Source: uow.edu.au

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"Optimising Automatic Text Classification Approach in Adaptive Online C" by Ya feng Zheng, Zhang hao Gao et al.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to ...
Adaptation Models Attention Mechanism Deep Learning Feature Extraction Long Short Term Memory Network Nline Collaborative Discussion
Source: uow.edu.au

Microsoft previews text classification API for ML.NET

Microsoft has unveiled a preview of the ML.NET Text Classification API, an API intended to make it easier to train custom text classification models using the open source ML.NET machine learning framework. Introduced June 14, the ML.NET Text Classification API uses “state-of-the-art” deep learning techniques, Microsoft said. ML.NET allows developers to integrate custom machine learning
Microsoft Research Bidirectional Encoder Representations Text Classification Visual Studio

Natural Language Processing (NLP) in Python with 8 Projects

[img]https://i.imgur.com/utpywTR.jpg[/img] [b]Natural Language Processing (NLP) in Python with 8 Projects[/b] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 4.72 GB | Duration: 10h 25m Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, De...
Google Colab Ankit Vijay Fasttext Library For Text Neural Network Based Language Processing Learn Natural Language Processing Python

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