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Transformer Models and BERT Model: NLP Advanced Course

Learn advanced NLP with Transformer and BERT models. Master text classification, Q&A, and natural language inference in just 1 hour.

Learn advanced NLP with Transformer and BERT models. Master text classification, Q&A, and natural language inference in just 1 hour.

This advanced course introduces you to the Transformer architecture and BERT (Bidirectional Encoder Representations from Transformers) model. You'll explore the key components of the Transformer architecture, including the self-attention mechanism, and how it's used to create the BERT model. The course covers various tasks that the BERT model can be applied to, such as text classification, question answering, and natural language inference. Designed for those already in the industry, this concise 1-hour course provides a deep dive into cutting-edge NLP techniques, equipping you with the knowledge to apply these powerful models in real-world scenarios.

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Transformer Models and BERT Model: NLP Advanced Course

This course includes

1 Hour

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

2,435

What you'll learn

  • Understand the key components of the Transformer architecture

  • Explore the self-attention mechanism in depth

  • Learn how the Transformer architecture is used to create the BERT model

  • Discover various NLP tasks that can be solved using the BERT model

  • Gain practical insights into applying BERT for text classification

  • Understand how BERT can be used for question answering tasks

Skills you'll gain

Transformer
BERT
NLP
text classification
question answering
self-attention
natural language inference

This course includes:

23 Minutes PreRecorded video

1 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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There is 1 module in this course

This course provides a comprehensive overview of Transformer models and the BERT (Bidirectional Encoder Representations from Transformers) model. Students will delve into the core components of the Transformer architecture, with a focus on the self-attention mechanism. The course explains how this architecture is utilized to construct the BERT model. Participants will learn about various Natural Language Processing (NLP) tasks that can be tackled using the BERT model, including text classification, question answering, and natural language inference. The curriculum is designed to provide both theoretical understanding and practical insights, enabling learners to apply these advanced NLP techniques in real-world scenarios.

Modelli Transformer e modello BERT: Panoramica

Module 1 · 51 Minutes to complete

Fee Structure

Payment options

Financial Aid

Instructor

Google Cloud Training
Google Cloud Training

4.7 rating

86 Reviews

26,85,892 Students

1,554 Courses

Empowering Businesses with Expert Training from Google Cloud

The Google Cloud Training team is tasked with developing, delivering, and evaluating training programs that enable our enterprise customers and partners to effectively utilize our products and solutions. Google Cloud empowers millions of organizations to enhance employee capabilities, improve customer service, and innovate for the future using cutting-edge technology built specifically for the cloud. Our products are designed with a focus on security, reliability, and scalability, covering everything from infrastructure to applications, devices, and hardware. Our dedicated teams are committed to helping customers successfully leverage our technologies to drive their success.

Transformer Models and BERT Model: NLP Advanced Course

This course includes

1 Hour

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

2,435

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.