BERT NLP Tutorial 1- Introduction | BERT Machine Learning | KGP Talkie




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In this video, I will explain the BERT research paper.
To understand transformers we first must understand the attention mechanism. The Attention mechanism enables the transformers to have extremely long term memory. A transformer model can “attend” or “focus” on all previous tokens that have been generated. Recurrent neural networks (RNN) are also capable of looking at previous inputs too. But the power of the attention mechanism is that it doesn’t suffer from short term memory. RNNs can theoretically access information arbitrarily far in the past, but in practice, they have a hard time keeping that information in their internal state.
BERT is designed to pre-train deep bidirectional representations from the unlabeled text by jointly conditioning on both left and right contexts in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
Proper language representation is key for general-purpose language understanding by machines. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary. For example, the word “bank” would have the same representation in “bank deposit” and in “riverbank”.Contextual models instead generate a representation of each word that is based on the other words in the sentence. BERT, as a contextual model, captures these relationships in a bidirectional way. BERT was built upon recent work and clever ideas in pre-training contextual representations including Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, the OpenAI Transformer, ULMFit, and the Transformer. Although these models are all unidirectional or shallowly bidirectional, BERT is fully bidirectional. In this sentiment analysis with BERT for python video, you will learn various aspects of sentiment analysis. To improve this model you can try sentiment analysis using xlnet.

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💯 Read Research Paper
https://arxiv.org/abs/1810.04805
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Comment List

  • KGP Talkie
    January 23, 2021

    Very nice tutorial

  • KGP Talkie
    January 23, 2021

    please provide a link of PPT

  • KGP Talkie
    January 23, 2021

    @KGP Talkie … how is your BERT course on Udemy different from these two videos of BERT in your NLP playlist? Thanks

  • KGP Talkie
    January 23, 2021

    you're the goat! thank you

  • KGP Talkie
    January 23, 2021

    This is too much superficial explanation..

  • KGP Talkie
    January 23, 2021

    BERT very well explained. Thank you, Tushar

  • KGP Talkie
    January 23, 2021

    What is L and What is 4H didn't get it.

  • KGP Talkie
    January 23, 2021

    Great!

  • KGP Talkie
    January 23, 2021

    Your videos are really the worthiest tutorials forever Sir👍👍

  • KGP Talkie
    January 23, 2021

    Thank you for the video! I was wondering if you could make a video about docBert or suggest a way to make document embedding for prediction tasks? Thank you!

  • KGP Talkie
    January 23, 2021

    Hi Laxmi kant, Good explanation/presentation and highly informative as usual. Kindly continue with some coding parts which will be helpful

  • KGP Talkie
    January 23, 2021

    Thanks you, for makeing videos on advanced topics

  • KGP Talkie
    January 23, 2021

    Plz do more videos on VERY task.
    Plz release asnp bro.
    Tq u brother.

  • KGP Talkie
    January 23, 2021

    Thank you sir for the videos and the efforts that you put in to deliver the knowledge to everyone is just awesome. Keep going and keep making wonderful videos sir 💯

  • KGP Talkie
    January 23, 2021

    KGP Talkie is the best

  • KGP Talkie
    January 23, 2021

    Thank you, Mr. Laxmi Kant Ji for this video tutorials !!!

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