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בקורס הזה נציג את הארכיטקטורה של טרנספורמרים ואת המודל של ייצוגים דו-כיווניים של מקודד מטרנספורמרים (BERT). תלמדו על החלקים השונים בארכיטקטורת הטרנספורמר, כמו מנגנון תשומת הלב, ועל התפקיד שלו בבניית מודל BERT. תלמדו גם על המשימות השונות שאפשר להשתמש ב-BERT כדי לבצע אותן, כמו סיווג טקסטים, מענה על שאלות והֶקֵּשׁ משפה טבעית. נדרשות כ-45 דקות כדי להשלים את הקורס הזה.
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Syllabus
מודלים של טרנספורמרים ומודל BERT: סקירה כללית
ביחידת הלימוד הזו תלמדו על החלקים השונים בארכיטקטורת הטרנספורמר, כמו מנגנון תשומת הלב, ועל התפקיד שלו בבניית מודל BERT. תלמדו גם על המשימות השונות שאפשר להשתמש ב-BERT כדי לבצע אותן, כמו סיווג טקסטים, מענה על שאלות והֶקֵּשׁ משפה טבעית.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores transformers and BERT, which is standard in natural language processing
Taught by Google Cloud Training, who are recognized for their work in transformers and BERT
Develops core skills for working with natural language processing
Requires no prior knowledge, making it a good starting point for beginners
Covers a range of topics, from the basics of transformers and BERT to advanced applications
Provides hands-on exercises to help learners apply what they learn
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Transformer Models and BERT Model - בעברית with these
activities:
Review the basics of transformers
Show steps
Refresh your knowledge of transformers, which will help you better understand the BERT model.
Browse courses on
Transformers
Show steps
-
Read a tutorial on transformers
-
Watch a video lecture on transformers
-
Complete a practice exercise on transformers
Follow a tutorial on using BERT for question answering
Show steps
Follow a tutorial on using BERT for question answering, which will help you learn how to use the model for a specific NLP task.
Browse courses on
BERT
Show steps
-
Find a tutorial on using BERT for question answering
-
Follow the steps in the tutorial
-
Complete the practice exercises in the tutorial
Practice using BERT for text classification
Show steps
Practice using BERT for text classification, which will help you reinforce your understanding of the model and its applications.
Browse courses on
BERT
Show steps
-
Find a dataset for text classification
-
Load the dataset into a BERT model
-
Train the BERT model on the dataset
-
Evaluate the performance of the BERT model
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a presentation on the applications of BERT
Show steps
Create a presentation on the applications of BERT, which will help you synthesize your knowledge of the model and its potential uses.
Browse courses on
BERT
Show steps
-
Research the applications of BERT
-
Organize your research into a presentation
-
Practice delivering your presentation
Develop a BERT model for a specific NLP task
Show steps
Develop a BERT model for a specific NLP task, which will help you apply your knowledge of the model to a real-world problem.
Browse courses on
BERT
Show steps
-
Define the NLP task
-
Collect and prepare a dataset for the task
-
Train a BERT model on the dataset
-
Evaluate the performance of the BERT model
-
Deploy the BERT model for use
Review the basics of transformers
Show steps
Refresh your knowledge of transformers, which will help you better understand the BERT model.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Read a tutorial on transformers
- Watch a video lecture on transformers
- Complete a practice exercise on transformers
Follow a tutorial on using BERT for question answering
Show steps
Follow a tutorial on using BERT for question answering, which will help you learn how to use the model for a specific NLP task.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Find a tutorial on using BERT for question answering
- Follow the steps in the tutorial
- Complete the practice exercises in the tutorial
Practice using BERT for text classification
Show steps
Practice using BERT for text classification, which will help you reinforce your understanding of the model and its applications.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Find a dataset for text classification
- Load the dataset into a BERT model
- Train the BERT model on the dataset
- Evaluate the performance of the BERT model
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a presentation on the applications of BERT
Show steps
Create a presentation on the applications of BERT, which will help you synthesize your knowledge of the model and its potential uses.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Research the applications of BERT
- Organize your research into a presentation
- Practice delivering your presentation
Develop a BERT model for a specific NLP task
Show steps
Develop a BERT model for a specific NLP task, which will help you apply your knowledge of the model to a real-world problem.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Define the NLP task
- Collect and prepare a dataset for the task
- Train a BERT model on the dataset
- Evaluate the performance of the BERT model
- Deploy the BERT model for use
Career center
Learners who complete Transformer Models and BERT Model - בעברית will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
For those looking to specialize in Natural Language Processing Engineering, this course provides a foundational understanding of two critical NLP models: Transformer Models and BERT. The course delves into the mechanics of BERT, demonstrating how BERT has overcome limitations of earlier NLP models. In short, this course will be of significant benefit to NLP Engineers looking to understand and apply industry standards in their work.
NLP Researcher
NLP Researcher
This course on Transformer Models and the BERT Model is a must-take for aspiring or practicing NLP Researchers. The course digs into the details of how BERT improved upon the original Transformer model. This understanding will enable NLP Researchers to push the boundaries of what is possible with natural language processing.
Research Scientist
Research Scientist
Research Scientists working in NLP will find this course on Transformer Models and the BERT Model to be extremely valuable. The course explains how BERT uses a Self-Attention mechanism to improve upon the Transformer Model. Research Scientists can then apply this learning to their own research efforts, leading to potential breakthroughs in NLP.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers seeking to develop in the NLP domain will find this course particularly valuable. The course goes over how BERT was built on top of Transformer Models, and the NLP tasks for which BERT is commonly used today. Additionally, course materials include detailed explanations of the Self-Attention mechanism used in BERT.
Computational Linguist
Computational Linguist
This course on Transformer Models and the BERT Model is directly relevant to the daily work of a Computational Linguist. Course materials explore the Self-Attention mechanism, which is a key advancement of the BERT model. Understanding Self-Attention is crucial to building a BERT model and as such is vital for Computational Linguistics research.
Language Technologist
Language Technologist
This course on Transformer Models and the BERT Model will be of great use to aspiring or current Language Technologists. By completing this course, Language Technologists will demonstrate initiative in staying current with industry best-practices in NLP. The course gives a clear explanation of how BERT is used to achieve state-of-the-art results in a wide variety of NLP tasks.
Data Scientist
Data Scientist
For Data Scientists seeking to become proficient in Natural Language Processing, this course on Transformer Models and the BERT Model will provide useful training. The course focuses on how the Self-Attention mechanism forms the foundation of BERT and discusses how BERT can be applied to a wide variety of NLP tasks. Whether an aspiring or practicing Data Scientist, this course can be very helpful.
Product Manager
Product Manager
Product Managers seeking to keep their teams competitive should take this course on Transformer Models and the BERT Model. The course explains the inner workings of BERT and shows how it is used in cutting-edge NLP applications. Product Managers with this knowledge will be able to effectively lead their teams to build innovative NLP products.
Consultant
Consultant
Consultants specializing in NLP will want to complete this course on Transformer Models and the BERT Model to stay on top of the NLP industry landscape. The course offers a detailed analysis of BERT, one of the most popular and effective NLP models. This knowledge will help Consultants better serve clients with NLP needs and deliver superior results.
Technical Writer
Technical Writer
Technical Writers working with NLP will benefit from completing this course on Transformer Models and the BERT Model. The course sheds light on the inner workings of BERT, allowing Technical Writers to more accurately describe and document NLP applications. In short, this course will empower aspiring and practicing Technical Writers to convey complex NLP concepts with clarity.
AI Engineer
AI Engineer
The study of Transformer Models and the BERT Model, with particular focus on the Self-Attention mechanism, is of direct relevance to an AI Engineer. This course focuses on the role of Self-Attention in building a BERT model and gives an overview of how BERT may be used to perform a wide variety of tasks including text classification, question answering, and natural language inference.
Software Engineer
Software Engineer
Software Engineers working on NLP applications can benefit from this course on Transformer Models and the BERT Model. Studying BERT can simplify and improve NLP implementations for a variety of software applications. Additionally, the course's focus on the Self-Attention mechanism will be of great benefit to Software Engineers working on image and audio data as well.
Business Analyst
Business Analyst
This course on Transformer Models and the BERT Model is a worthwhile investment for Business Analysts looking to expand into the field of NLP. The course will help build a foundation for understanding how BERT can be applied to text-based business applications such as customer support chatbots and sentiment analysis dashboards.
Data Analyst
Data Analyst
For Data Analysts seeking to specialize in NLP for text analysis, this course offers a deep dive into two key models: Transformer Models and BERT. The course covers how BERT has improved upon the original Transformer model, and also outlines current NLP applications of BERT. Whether seeking to excel in the NLP subfield or to incorporate NLP into a diverse data analysis practice, this course may be useful.
Quantitative Analyst
Quantitative Analyst
For Quantitative Analysts seeking to incorporate NLP into their work, this course on Transformer Models and the BERT Model provides a great starting point. The course shows how BERT can be used to perform complex NLP tasks such as text classification and named entity recognition. With this knowledge, Quantitative Analysts can incorporate NLP into existing financial models or develop new ones altogether.
For more career information including salaries, visit:
OpenCourser.com/course/4avg1a/transformer
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Transformer Models and BERT Model - בעברית.
Provides a comprehensive overview of deep learning techniques for NLP, including transformer models. It offers additional insights and applications.
Provides a comprehensive overview of pattern recognition and machine learning, including transformer models. It offers additional depth and insights into the theoretical underpinnings of the course's content.
Provides a comprehensive overview of speech and language processing, including transformer models. It offers a broader perspective and additional insights into NLP.
Provides a comprehensive overview of statistical learning methods, including those used in NLP. It offers a strong foundation for understanding the statistical aspects of transformer models.
Provides a strong foundation in the mathematical concepts underlying machine learning, including transformers. It valuable reference for understanding the technical aspects of the model.
Provides a comprehensive introduction to neural network methods for NLP, including transformer models. It serves as a valuable reference for additional background knowledge.
Provides a broader perspective on language processing, exploring the role of knowledge and context in understanding language. It adds depth to the course's focus on models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4avg1a/transformer
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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
עברית
Good to know
Explores transformers and BERT, which is standard in natural language processing
Taught by Google Cloud Training, who are recognized for their work in transformers and BERT
Develops core skills for working with natural language processing
Requires no prior knowledge, making it a good starting point for beginners
Covers a range of topics, from the basics of transformers and BERT to advanced applications
Provides hands-on exercises to help learners apply what they learn
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