Difference between revisions of "Text Transfer Learning"

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[http://www.youtube.com/results?search_query=Text+document+speech+words+Transfer+Learning+machine+neural+network YouTube search...]
 
[http://www.youtube.com/results?search_query=Text+document+speech+words+Transfer+Learning+machine+neural+network YouTube search...]
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* [[Learning Techniques]]
 
* [[Learning Techniques]]
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* [[Image/Video Transfer Learning]]
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* [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]]
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* [[Transfer Learning]]
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* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
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* [[Causation vs. Correlation]] ... [[Autocorrelation]] ...[[Convolution vs. Cross-Correlation (Autocorrelation)]]
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
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* [http://venturebeat.com/2019/10/24/google-achieves-state-of-the-art-nlp-performance-with-an-enormous-language-model-and-data-set/ Google achieves state-of-the-art NLP performance with an enormous language model and data set | Kyle Wiggers - Venture Beat] researchers at Google developed a new data set — Colossal Clean Crawled Corpus — and a unified framework and model dubbed [http://arxiv.org/pdf/1910.10683.pdf Text-to-Text Transformer] that converts language problems into a text-to-text format. Colossal Clean Crawled Corpus — were sourced from the Common Crawl project, which scrapes roughly 20 terabytes of English text from the web each month.
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* [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 
* [http://pdfs.semanticscholar.org/1bb2/39731589f3114a3fe5b35e42a635b5eacb38.pdf  Transfer Learning for Text Mining | Weike Pan, Erheng Zhong, and Qiang Yang]  
 
* [http://pdfs.semanticscholar.org/1bb2/39731589f3114a3fe5b35e42a635b5eacb38.pdf  Transfer Learning for Text Mining | Weike Pan, Erheng Zhong, and Qiang Yang]  
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* [[Natural Language Processing (NLP)]]
 
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* [http://venturebeat.com/2019/10/24/google-achieves-state-of-the-art-nlp-performance-with-an-enormous-language-model-and-data-set/ Google achieves state-of-the-art NLP performance with an enormous language model and data set | Kyle Wiggers - Venture Beat] researchers at Google developed a new data set — Colossal Clean Crawled Corpus — and a unified framework and model dubbed [http://arxiv.org/pdf/1910.10683.pdf Text-to-Text Transformer] that converts language problems into a text-to-text format. Colossal Clean Crawled Corpus — were sourced from the Common Crawl project, which scrapes roughly 20 terabytes of English text from the web each month.
 
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* [[Generative Pre-trained Transformer-2 (GPT-2)]]
 
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* [[Learning Techniques]]
 
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Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) [http://blogs.technet.microsoft.com/machinelearning/2018/04/25/transfer-learning-for-text-using-deep-learning-virtual-machine-dlvm/ Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft]
 
Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) [http://blogs.technet.microsoft.com/machinelearning/2018/04/25/transfer-learning-for-text-using-deep-learning-virtual-machine-dlvm/ Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft]
  
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<youtube>zxJJ0T54HX8</youtube>
 
<youtube>zxJJ0T54HX8</youtube>
 
<youtube>qN9hHlZKIL4</youtube>
 
<youtube>qN9hHlZKIL4</youtube>

Latest revision as of 15:35, 28 April 2024

YouTube search... ...Google search

Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft


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