Home>Courses>Getting Started with Google BERT

Intermediate

25h

Certificate of Completion

Getting Started with Google BERT

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

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Explanations

Adaptive Learning

AI-POWERED

Explanations

Adaptive Learning

This course includes

120 Lessons

26 Playgrounds

12 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Recommendations

Course 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

TAKEAWAY SKILLS

Transformer Models

Machine Learning

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

Course Content

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

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

Course Author

Packt

Project

Build a Language Detector

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Build a Language Detector

Create a language detection application in Python, that uses n-grams to identify languages in text documents, allowing easy expansion for more languages.

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1 hour

Project

Vision Transformer for Image Classification

advanced

2 hour

Project

Vision Transformer for Image Classification

Learn to train a Vision Transformer for classifying images from the MNIST dataset, including data preprocessing, augmentation, and evaluation.

advanced

2 hours

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Frequently Asked Questions

Is BERT better than GPT?

Whether to choose BERT or GPT depends on the context and task, as both models are designed with different purposes and strengths. BERT is best suited for tasks that focus on understanding or classifying text, making it ideal when interpretability and model efficiency are important. On the other hand, GPT excels in tasks that require coherent text generation or conversational capabilities, offering versatility across a wide range of text-based applications.

Is Google BERT free to use?

Google open-sourced the pretrained BERT models and the associated code in 2018, allowing developers, researchers, and organizations to use and fine-tune BERT for various natural language processing (NLP) tasks. The code and pretrained models can be accessed on its official GitHub repository. However, running BERT may incur compute costs, especially for training or fine-tuning.

Is ChatGPT based on BERT?

No, ChatGPT is not based on BERT. BERT is designed for understanding text, such as classification or question-answering. It looks at the full context of a sentence (bidirectional) and uses the encoder part of the transformer. ChatGPT is built for generating text and conversation. It predicts the next word in a sequence (unidirectional) and uses the decoder part of the transformer.

Is BERT a CNN model?

No, BERT is a transformer-based model that uses self-attention mechanisms, not convolutional layers like CNNs.

Is BERT an autoencoder?

No, BERT is not an autoencoder. Traditional autoencoders compress the entire input into a latent representation and reconstruct it fully, while BERT only predicts the masked parts using a bidirectional context.

Is BERT faster than LSTM?

BERT is generally faster than LSTM for many tasks. BERT uses parallel processing with its transformer architecture, while LSTM processes data sequentially, making it slower, especially for long sequences. However, BERT is much more computationally intensive than LSTM, requiring more memory and powerful hardware (like GPUs) to run efficiently.

What is the difference between the BERT and Bart model?

BERT is an encoder-only model designed for understanding text. It’s trained to predict masked words using both left and right context, making it great for classification and question answering tasks.

BART combines encoder and decoder components, making text generation and understanding suitable. It’s trained to reconstruct corrupted text, excelling at tasks like summarization and translation.