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blog.acolyer.org | ||
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www.mlpowered.com
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| | | | | Blog posts and other information | |
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mccormickml.com
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| | | | | [AI summary] The tutorial provides a comprehensive guide to extracting and analyzing BERT embeddings. It begins with tokenization and segment embedding creation, followed by the calculation of word and sentence embeddings using different strategies such as summation and averaging of hidden layers. The context-dependent nature of BERT embeddings is demonstrated by comparing vectors for the word 'bank' in different contexts. The tutorial also discusses pooling strategies, layer choices, and the importance of context in generating meaningful embeddings. It concludes with considerations for special tokens, out-of-vocabulary words, similarity metrics, and implementation options. | |
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towardsml.wordpress.com
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| | | | | How is Google Translate able to understand text and convert it from one language to another? How do you make a computer understand that in the context of IT Apple is a company and not a fruit? Or how is a Smart-Keypad able to predict the most likely next few words that you are going... | |
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www.analyticsvidhya.com
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| | | Explore RNNs: their unique architecture, working principles, BPTT, pros/cons, and Python implementation using Keras. | ||
