Скачать с ютуб What is BERT? | BERT Explained | BERT Transformer & Embedding Understanding with Example в хорошем качестве

What is BERT? | BERT Explained | BERT Transformer & Embedding Understanding with Example

What is BERT? | BERT Explained | BERT Transformer & Embedding Understanding with Example 🔥Liverpool John Moore University MS In Data Science: https://www.upgrad.com/data-science-m... 🔥IIIT Bangalore Post Graduate Program in Data Science & AI: https://www.upgrad.com/data-science-p... 🔥Graduate Certificate Programme in Data Science & AI: https://www.upgrad.com/graduate-certi... 🔥Become a Skilled and Job-Ready Data Scientist: https://www.upgrad.com/bootcamps/job-... Discover the intricacies of BERT, one of the most advanced models in natural language processing, with our detailed tutorial, "What is BERT?," brought to you by upGrad. This video is crafted to provide a deep understanding of BERT (Bidirectional Encoder Representations from Transformers), its components, and its practical applications. 1. Encoders and Decoders: Dive into the architecture of transformers, focusing on the roles of encoders and decoders. Learn how BERT uses encoders to process input text bidirectionally, capturing the context from both left and right, unlike traditional models that read text sequentially. This section will explain the fundamental building blocks of BERT and how they contribute to its powerful contextual understanding. 2. Next Sentence Prediction : Explore one of the pre-training tasks of BERT: Next Sentence Prediction (NSP). Understand how BERT is trained to predict the relationship between pairs of sentences, enabling it to perform tasks like question answering and text classification more effectively. This section will provide examples and explain the importance of NSP in enhancing BERT's capabilities. 3. What is Fine Tuning ? Learn about the fine-tuning process, where a pre-trained BERT model is further trained on a specific task with task-specific data. Understand the steps involved in fine-tuning, and how it allows BERT to adapt to a wide range of NLP applications, from sentiment analysis to named entity recognition. This section will highlight the flexibility and adaptability of BERT through fine-tuning. 4. Sentence Embeddings : We conclude with an exploration of sentence embeddings in BERT. Discover how BERT generates high-quality embeddings that capture the semantic meaning of entire sentences. Understand the applications of these embeddings in various NLP tasks and how they improve the performance of downstream applications. Key Moments : 00:00 - Introduction to BERT 08:41 - Encoders and Decoders 16:13 - Next Sentence Prediction 19:03 - What is Fine Tuning 25:00 - Input Layer 26:41 - Sentence Embeddings By the end of this What is BERT video, you'll have a comprehensive understanding of BERT, its architecture, and its applications. Join us in this deep dive into BERT and unlock the potential of advanced NLP with upGrad. Watch now and elevate your knowledge of natural language processing! #WhatisBERT #BERTExplained #BERTTransformer #BERTEmbedding #BERT #BidirectionalEncoderRepresentationsfromTransformers