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Getting Started with Google BERT
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Explore Google BERT, fine-tune NLP tasks, discover variants, and build real-world applications with cutting-edge transformer models.

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Overview

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 alongsi...Show More

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

TAKEAWAY SKILLS

Transformer Models

Machine Learning

Content

120 Lessons12 Quizzes

Before We Start

1 Lessons

Get familiar with Google's BERT architecture for NLP tasks and fine-tuning methods.

About the Course

Starting Off with BERT

1 Lessons

Look at BERT’s architecture, pre-training tasks, and applications in NLP tasks.

Introduction: BERT

A Primer on Transformers

18 Lessons

Work your way through the transformer architecture, including encoder-decoder components and self-attention mechanisms.

Introduction to the Transformer

Understanding the Encoder of the Transformer

Self-Attention Mechanism

Understanding the Self-Attention Mechanism

Multi-Head Attention Mechanism

Learning Position with Positional Encoding

Encoder: Feedforward and Add and Norm Component

Putting All the Encoder Components Together

Understanding the Decoder of the Transformer

Masked Multi-Head Attention

Multi-Head Attention

Decoder: Feedforward and Add and Norm Component

Putting All the Decoder Components Together

Putting the Encoder and Decoder Together

Summary: A Primer on Transformers

Exercise: A Primer on Transformers

Exercise: Decoder Architecture

Quiz: A Primer on Transformers

Understanding the BERT Model

14 Lessons

Grasp the fundamentals of the BERT model's architecture, training, and tokenization methods.

Introduction to the BERT Model

Working of BERT

Configurations of BERT

Pre-Training the BERT Model

The WordPiece Tokenizer

Pre-Training Strategies for the BERT Model

Masked Language Modeling

Next Sentence Prediction

Pre-Training Procedure

Subword Tokenization Algorithms

Byte Pair Encoding

Byte-Level Byte Pair Encoding and WordPiece Algorithms

Summary: Understanding the BERT Model

Quiz: Understanding the BERT Model

Getting Hands-On with BERT

11 Lessons

Solve problems in applying pre-trained BERT for various NLP tasks using embeddings.

Exploring the Pre-Trained BERT Model

Transformers

Extracting Embeddings From All Encoder Layers of BERT

Fine-Tuning BERT for Downstream Tasks

Sentiment Analysis

Natural Language Interface

Question-Answering

Performing Question-Answering with the Fine-Tuned BERT

Named Entity Recognition

Summary: Getting Hands-On with BERT

Quiz: Getting Hands-On with BERT

Exploring BERT Variants

1 Lessons

Focus on notable BERT variants and their architectural enhancements for efficient performance.

Introducion: BERT Variants

Different BERT Variants

12 Lessons

Practice using ALBERT, RoBERTa, ELECTRA, and SpanBERT for task-specific NLP improvements.

ALBERT

ALBERT : Training the Model

ALBERT : Embeddings Extraction

RoBERTa

RoBERTa Tokenizer

ELECTRA

Generator and Discriminator of the ELECTRA Model

Training the ELECTRA Model

SpanBERT

SpanBERT: Exploring Architecture

Summary: Different BERT Variants

Quiz: Different BERT Variants

BERT Variants—Based on Knowledge Distillation

14 Lessons

Try out knowledge distillation in BERT variants, including DistilBERT and TinyBERT.

Knowledge Distillation

Training the Student Network

DistilBERT

Training the Student BERT (DistilBERT)

TinyBERT

Teacher-Student Architecture

Distillation of Transformer Layer

Distillation of Embedding and Prediction Layer

Distillation Techniques for Pre-training and Fine-tuning

Data Augmentation

Transferring Knowledge from BERT to Neural Networks

The Data Augmentation Methods

Summary: BERT Variants—Based on Knowledge Distillation

Quiz: BERT Variants—Based on Knowledge Distillation

Applications of BERT

1 Lessons

Look at BERT's diverse applications in text summarization, multilingual tasks, and specialized fields.

Introduction: Applications of BERT

10.

Exploring BERTSUM for Text Summarization

8 Lessons

Examine text summarization and fine-tuning BERTSUM for extractive and abstractive summaries.

Text Summarization

Fine-Tuning BERT for Extractive Summarization

BERTSUM for Extractive Summary

BERTSUM for Abstractive Summarization

ROUGE Evaluation Metrics

Training the BERTSUM Model

Summary: Exploring BERTSUM for Text Summarization

Quiz: Exploring BERTSUM for Text Summarization

Semantic Search with Transformers

Project

11.

Applying BERT to Other Languages

18 Lessons

Grasp the fundamentals of utilizing multilingual and monolingual BERT models in various languages.

Understanding Multilingual BERT

Evaluating M-BERT on the NLI task

How Multilingual is Multilingual BERT?

M-BERT Generalization

Effect of Code-Switching and Transliteration

The Cross-Lingual Language Model (XLM)

The XLM-R Model

Language-Specific BERT

BERT Models for Italian and Portuguese

RuBERT for Russian

Summary: Applying BERT to Other Languages

Quiz: Applying BERT to Other Languages

12.

Exploring Sentence and Domain-Specific BERT

10 Lessons

Dig into Sentence-BERT enhancements and domain-specific adaptations like ClinicalBERT and BioBERT.

Sentence-BERT

Sentence-BERT with a Siamese Network

Sentence-BERT with a Triplet Network

Use Cases of Sentence-BERT Model

Learning Multilingual Embeddings Through Knowledge Distillation

Multilingual Sentence-BERT Model

Domain-Specific BERT

BioBERT

Summary: Exploring Sentence and Domain-Specific BERT

Quiz: Exploring Sentence and Domain-Specific BERT

13.

Working with VideoBERT, BART, and More

10 Lessons

See how VideoBERT integrates video and language, and explore BART's text, document summation.

VideoBERT Model

Pre-training Dataset and Applications of VideoBERT

BART Model

Noising Techniques

Performing Text Summarization with BART

Exploring BERT Libraries

Building a Document Answering Model

Document Summarization

Summary: Working with VideoBERT, BART, and More

Quiz: Working with VideoBERT, BART, and More

14.

Conclusion

1 Lessons

Approach Google BERT for state-of-the-art NLP applications and innovative projects.

What We've Learned

Similarity Detection in English Language Using RoBERTa

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Packt

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Getting Started with Google BERT Save

Getting Started with Google BERT
Save