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Getting Started with Google BERT - AI-Powered Course
Overview
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This comprehensive course dives into Google’s BERT architecture, exploring its revolutionary role in natural language processing (NLP). Starting with BERT’s architecture and pre-training methods, you’ll uncover the mechanics of transformers, including encoder-decoder components and self-attention mechanisms. Gain hands-on experience fine-tuning BERT for NLP tasks like sentiment analysis, question-answering, and named entity recognition.
Discover BERT variants such as ALBERT, RoBERTa, and DistilBERT alongside domain-specific adaptations like ClinicalBERT and BioBERT. Explore applications in text summarization, multilingual tasks, and advanced models like VideoBERT and BART. With practical coding exercises and quizzes, you’ll master embeddings, tokenization, and BERT libraries, equipping you to build cutting-edge NLP solutions.
Whether you’re new to Google BERT or enhancing your expertise, this course is your guide to state-of-the-art NLP innovations.
This comprehensive course dives into Google’s BERT architecture, exploring its revolutionary role in natural language processing...Show More
WHAT YOU'LL LEARN
An understanding of Google BERT’s architecture, pre-training tasks (MLM, NSP), and transformer fundamentals like self-attention and multi-head attention
The ability to apply and fine-tune pretrained BERT models for NLP tasks such as sentiment analysis, NER, question answering, and domain-specific applications
Familiarity with BERT variants (ALBERT, RoBERTa, ELECTRA) and lightweight models using knowledge distillation (DistilBERT, TinyBERT)
The ability to utilize advanced BERT applications, including text summarization (BERTSUM), multilingual models (M-BERT), and multimodal tools like VideoBERT
The ability to build real-world projects using BERT libraries like Hugging Face Transformers and apply domain-specific models like BioBERT and FinBERT
An understanding of Google BERT’s architecture, pre-training tasks (MLM, NSP), and transformer fundamentals like self-attention and multi-head attention
Show more
TAKEAWAY SKILLS
Transformer Models
Machine Learning
Content
120 Lessons12 Quizzes
1.
Before We Start
1 Lessons
Get familiar with Google's BERT architecture for NLP tasks and fine-tuning methods.
2.
Starting Off with BERT
1 Lessons
Look at BERT’s architecture, pre-training tasks, and applications in NLP tasks.
3.
A Primer on Transformers
18 Lessons
Work your way through the transformer architecture, including encoder-decoder components and self-attention mechanisms.
4.
Understanding the BERT Model
14 Lessons
Grasp the fundamentals of the BERT model's architecture, training, and tokenization methods.
5.
Getting Hands-On with BERT
11 Lessons
Solve problems in applying pre-trained BERT for various NLP tasks using embeddings.
6.
Exploring BERT Variants
1 Lessons
Focus on notable BERT variants and their architectural enhancements for efficient performance.
7.
Different BERT Variants
12 Lessons
Practice using ALBERT, RoBERTa, ELECTRA, and SpanBERT for task-specific NLP improvements.
8.
BERT Variants—Based on Knowledge Distillation
14 Lessons
Try out knowledge distillation in BERT variants, including DistilBERT and TinyBERT.
9.
Applications of BERT
1 Lessons
Look at BERT's diverse applications in text summarization, multilingual tasks, and specialized fields.
10.
Exploring BERTSUM for Text Summarization
8 Lessons
Examine text summarization and fine-tuning BERTSUM for extractive and abstractive summaries.
11.
Applying BERT to Other Languages
18 Lessons
Grasp the fundamentals of utilizing multilingual and monolingual BERT models in various languages.
12.
Exploring Sentence and Domain-Specific BERT
10 Lessons
Dig into Sentence-BERT enhancements and domain-specific adaptations like ClinicalBERT and BioBERT.
13.
Working with VideoBERT, BART, and More
10 Lessons
See how VideoBERT integrates video and language, and explore BART's text, document summation.
14.
Conclusion
1 Lessons
Approach Google BERT for state-of-the-art NLP applications and innovative projects.
Certificate of Completion
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