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Ce cours présente l'architecture Transformer et le modèle BERT (Bidirectional Encoder Representations from Transformers). Vous découvrirez quels sont les principaux composants de l'architecture Transformer, tels que le mécanisme d'auto-attention, et comment ils sont utilisés pour créer un modèle BERT. Vous verrez également les différentes tâches pour lesquelles le modèle BERT peut être utilisé, comme la classification de texte, les questions-réponses et l'inférence en langage naturel. Ce cours dure environ 45 minutes.
Register for this course and see more details by visiting:
OpenCourser.com/course/av9waq/transformer
What's inside
Syllabus
Modèles Transformer et modèle BERT : présentation
Dans ce module, vous découvrirez quels sont les principaux composants de l'architecture Transformer, tels que le mécanisme d'auto-attention, et comment ils sont utilisés pour créer un modèle BERT. Vous verrez également les différentes tâches pour lesquelles le modèle BERT peut être utilisé, comme la classification de texte, les questions-réponses et l'inférence en langage naturel.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ce cours présente les bases de l'architecture Transformer, un modèle d'apprentissage automatique à la pointe de la technologie en traitement du langage naturel
Le cours explique en détail le fonctionnement du mécanisme d'auto-attention, un composant clé de l'architecture Transformer
Le cours explore les applications pratiques du modèle BERT, y compris la classification de texte, les questions-réponses et l'inférence en langage naturel
Le cours est dispensé par Google Cloud Training, une équipe reconnue pour son expertise en intelligence artificielle et en traitement du langage naturel
Le cours s'adresse aux développeurs, aux scientifiques des données et aux autres professionnels qui souhaitent approfondir leurs connaissances en traitement du langage naturel
Le cours est accessible à différents niveaux d'expérience, convenant aussi bien aux débutants qu'aux apprenants intermédiaires
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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 - Français with these
activities:
Review Latent Dirichlet Allocation
Show steps
Refreshing your knowledge of Latent Dirichlet Allocation will help you understand the underlying concepts of the Transformer architecture and BERT model.
Browse courses on
Latent Dirichlet Allocation
Show steps
-
Read the Wikipedia article on Latent Dirichlet Allocation
-
Review the LDA model in a textbook or online resource
-
Work through some practice problems on LDA
Review transformer architecture basics
Show steps
Revisit the fundamentals of transformer architecture to strengthen your understanding of the upcoming BERT model.
Browse courses on
Transformer Architecture
Show steps
-
Read the introductory sections on transformer architecture.
-
Create a simple diagram of a transformer encoder.
Connect with BERT experts
Show steps
Seek guidance from experienced professionals in the field of BERT. Their insights and advice can accelerate your learning and provide valuable perspectives.
Show steps
-
Attend industry events or online forums related to BERT.
-
Reach out to researchers or practitioners working in the field.
-
Request mentorship or guidance on your BERT-related projects or goals.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Practice Using Hugging Face Transformers Library
Show steps
Practicing with the Hugging Face Transformers library will help you become proficient in using this powerful tool for building NLP models.
Browse courses on
Hugging Face Transformers
Show steps
-
Install the Hugging Face Transformers library
-
Load and tokenize a dataset using the Transformers library
-
Train a BERT model using the Transformers library
-
Evaluate the performance of your BERT model
Compile BERT resources
Show steps
Gather a collection of valuable materials on BERT, including tutorials, articles, and datasets, to enhance your learning experience.
Show steps
-
Search for and bookmark relevant BERT tutorials and articles.
-
Explore public datasets related to BERT.
-
Organize your collection into a structured format.
Attend a Study Group on Transformer Models
Show steps
Attending a study group on Transformer models will allow you to discuss the concepts with other students and reinforce your understanding.
Browse courses on
Transformer Models
Show steps
-
Find a study group or organize your own
-
Prepare for the study group by reviewing the course materials
-
Participate actively in the study group discussion
BERT tasks practice
Show steps
Engage in hands-on exercises to reinforce your understanding of BERT's capabilities. This will enhance your ability to apply BERT effectively.
Show steps
-
Find online platforms or tutorials offering BERT practice problems.
-
Attempt a variety of BERT tasks, such as text classification, question answering, and natural language inference.
-
Review your results and identify areas for improvement.
Create a Visual Representation of the Transformer Architecture
Show steps
Creating a visual representation of the Transformer architecture will help you understand the flow of information through the model.
Browse courses on
Transformer Architecture
Show steps
-
Draw a diagram of the Transformer architecture
-
Annotate the diagram with key components and their functions
-
Share your diagram with others and explain the Transformer architecture
Follow a Tutorial on Fine-Tuning a BERT Model
Show steps
Following a tutorial on fine-tuning a BERT model will help you learn how to apply the model to your own NLP tasks.
Browse courses on
BERT Model
Show steps
-
Find a tutorial on how to fine-tune a BERT model
-
Follow the tutorial step-by-step
-
Experiment with different hyperparameters and datasets
BERT implementation tutorials
Show steps
Delve deeper into BERT's implementation by following guided tutorials. This will provide you with practical experience and strengthen your understanding of the model's inner workings.
Show steps
-
Identify comprehensive tutorials that cover BERT implementation.
-
Follow the tutorials step-by-step, implementing BERT in your chosen programming language.
-
Test your implementation on sample datasets.
Contribute to BERT open-source projects
Show steps
Engage with the BERT community by contributing to open-source projects. This will provide hands-on experience and deepen your understanding of the model.
Show steps
-
Explore open-source BERT projects on platforms like GitHub.
-
Identify areas where you can contribute your skills.
-
Submit code contributions or documentation improvements.
BERT project application
Show steps
Apply your acquired knowledge by developing a project that utilizes BERT. This will demonstrate your proficiency and enhance your portfolio.
Show steps
-
Identify a problem or challenge that BERT can address.
-
Design and implement a solution using BERT.
-
Evaluate the results and present your findings.
Review Latent Dirichlet Allocation
Show steps
Refreshing your knowledge of Latent Dirichlet Allocation will help you understand the underlying concepts of the Transformer architecture and BERT model.
Browse courses on
Latent Dirichlet Allocation
Show steps
Show steps
Show steps
- Read the Wikipedia article on Latent Dirichlet Allocation
- Review the LDA model in a textbook or online resource
- Work through some practice problems on LDA
Review transformer architecture basics
Show steps
Revisit the fundamentals of transformer architecture to strengthen your understanding of the upcoming BERT model.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Read the introductory sections on transformer architecture.
- Create a simple diagram of a transformer encoder.
Connect with BERT experts
Show steps
Seek guidance from experienced professionals in the field of BERT. Their insights and advice can accelerate your learning and provide valuable perspectives.
Show steps
Show steps
Show steps
- Attend industry events or online forums related to BERT.
- Reach out to researchers or practitioners working in the field.
- Request mentorship or guidance on your BERT-related projects or goals.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Practice Using Hugging Face Transformers Library
Show steps
Practicing with the Hugging Face Transformers library will help you become proficient in using this powerful tool for building NLP models.
Browse courses on
Hugging Face Transformers
Show steps
Show steps
Show steps
- Install the Hugging Face Transformers library
- Load and tokenize a dataset using the Transformers library
- Train a BERT model using the Transformers library
- Evaluate the performance of your BERT model
Compile BERT resources
Show steps
Gather a collection of valuable materials on BERT, including tutorials, articles, and datasets, to enhance your learning experience.
Show steps
Show steps
Show steps
- Search for and bookmark relevant BERT tutorials and articles.
- Explore public datasets related to BERT.
- Organize your collection into a structured format.
Attend a Study Group on Transformer Models
Show steps
Attending a study group on Transformer models will allow you to discuss the concepts with other students and reinforce your understanding.
Browse courses on
Transformer Models
Show steps
Show steps
Show steps
- Find a study group or organize your own
- Prepare for the study group by reviewing the course materials
- Participate actively in the study group discussion
BERT tasks practice
Show steps
Engage in hands-on exercises to reinforce your understanding of BERT's capabilities. This will enhance your ability to apply BERT effectively.
Show steps
Show steps
Show steps
- Find online platforms or tutorials offering BERT practice problems.
- Attempt a variety of BERT tasks, such as text classification, question answering, and natural language inference.
- Review your results and identify areas for improvement.
Create a Visual Representation of the Transformer Architecture
Show steps
Creating a visual representation of the Transformer architecture will help you understand the flow of information through the model.
Browse courses on
Transformer Architecture
Show steps
Show steps
Show steps
- Draw a diagram of the Transformer architecture
- Annotate the diagram with key components and their functions
- Share your diagram with others and explain the Transformer architecture
Follow a Tutorial on Fine-Tuning a BERT Model
Show steps
Following a tutorial on fine-tuning a BERT model will help you learn how to apply the model to your own NLP tasks.
Browse courses on
BERT Model
Show steps
Show steps
Show steps
- Find a tutorial on how to fine-tune a BERT model
- Follow the tutorial step-by-step
- Experiment with different hyperparameters and datasets
BERT implementation tutorials
Show steps
Delve deeper into BERT's implementation by following guided tutorials. This will provide you with practical experience and strengthen your understanding of the model's inner workings.
Show steps
Show steps
Show steps
- Identify comprehensive tutorials that cover BERT implementation.
- Follow the tutorials step-by-step, implementing BERT in your chosen programming language.
- Test your implementation on sample datasets.
Contribute to BERT open-source projects
Show steps
Engage with the BERT community by contributing to open-source projects. This will provide hands-on experience and deepen your understanding of the model.
Show steps
Show steps
Show steps
- Explore open-source BERT projects on platforms like GitHub.
- Identify areas where you can contribute your skills.
- Submit code contributions or documentation improvements.
BERT project application
Show steps
Apply your acquired knowledge by developing a project that utilizes BERT. This will demonstrate your proficiency and enhance your portfolio.
Show steps
Show steps
Show steps
- Identify a problem or challenge that BERT can address.
- Design and implement a solution using BERT.
- Evaluate the results and present your findings.
Career center
Learners who complete Transformer Models and BERT Model - Français will develop knowledge and skills
that may be useful to these careers:
Computational Linguist
Computational Linguist
Computational Linguists use their expertise in linguistics and computer science to study the computational aspects of human language. This course may be useful for Computational Linguists as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve computational linguistics models for a variety of tasks, such as natural language processing and machine translation.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers use their expertise in computer science and linguistics to design and implement systems that can understand and generate human language. This course may be useful for Natural Language Processing Engineers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve natural language processing systems for a variety of tasks, such as machine translation and text classification.
Data Scientist
Data Scientist
Data Scientists use their skills in statistics, programming, and machine learning to extract insights from data. This course may be useful for Data Scientists as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve data science models for a variety of tasks, such as natural language processing and computer vision.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their expertise in programming, mathematics, and statistics to design, implement, and maintain machine learning algorithms. This course may be useful for Machine Learning Engineers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve machine learning models for a variety of tasks, such as natural language processing and computer vision.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve software systems for a variety of tasks, such as natural language processing and computer vision.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve products that use natural language processing and machine learning.
Business Analyst
Business Analyst
Business Analysts use their skills in data analysis and business strategy to help organizations make better decisions. This course may be useful for Business Analysts as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve business strategies that use natural language processing and machine learning.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and implementation of sales strategies. This course may be useful for Sales Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve sales strategies that use natural language processing and machine learning.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for the development and implementation of customer success strategies. This course may be useful for Customer Success Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve customer success strategies that use natural language processing and machine learning.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and implementation of marketing campaigns. This course may be useful for Marketing Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve marketing campaigns that use natural language processing and machine learning.
Operations Manager
Operations Manager
Operations Managers are responsible for the planning and execution of operations. This course may be useful for Operations Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve operations plans that use natural language processing and machine learning.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. This course may be useful for Technical Writers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve technical documentation that uses natural language processing and machine learning.
Project Manager
Project Manager
Project Managers are responsible for the planning and execution of projects. This course may be useful for Project Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve project plans that use natural language processing and machine learning.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the development and implementation of human resources strategies. This course may be useful for Human Resources Managers as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve human resources strategies that use natural language processing and machine learning.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in finance and data analysis to advise businesses on financial decisions. This course may be useful for Financial Analysts as it provides an introduction to the Transformer architecture and the BERT model. This knowledge can be applied to develop and improve financial models that use natural language processing and machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/av9waq/transformer
Reading list
We've selected six 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 - Français.
Ce livre fournit une introduction complète au traitement du langage naturel avec l'apprentissage profond. Il couvre les modèles Transformer, ainsi que d'autres architectures importantes.
Ce livre fournit un guide pratique de la mise en œuvre de modèles Transformer et d'autres algorithmes de traitement du langage naturel à l'aide de PyTorch.
Ce livre fournit une introduction complète à l'intelligence artificielle, y compris les modèles Transformer. Il est considéré comme un ouvrage de référence classique dans le domaine.
Ce livre fournit une introduction complète à l'apprentissage automatique. Il couvre les modèles Transformer, ainsi que d'autres algorithmes importants.
Ce livre fournit un guide pratique de la mise en œuvre de modèles Transformer et d'autres algorithmes d'apprentissage profond à l'aide de Python.
Ce livre fournit une introduction complète au traitement du langage naturel pour les informaticiens. Il couvre les modèles Transformer, ainsi que d'autres algorithmes importants.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/av9waq/transformer
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Effort
1 week of study, 6-8 hours
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Transcript
English
Good to know
Ce cours présente les bases de l'architecture Transformer, un modèle d'apprentissage automatique à la pointe de la technologie en traitement du langage naturel
Le cours explique en détail le fonctionnement du mécanisme d'auto-attention, un composant clé de l'architecture Transformer
Le cours explore les applications pratiques du modèle BERT, y compris la classification de texte, les questions-réponses et l'inférence en langage naturel
Le cours est dispensé par Google Cloud Training, une équipe reconnue pour son expertise en intelligence artificielle et en traitement du langage naturel
Le cours s'adresse aux développeurs, aux scientifiques des données et aux autres professionnels qui souhaitent approfondir leurs connaissances en traitement du langage naturel
Le cours est accessible à différents niveaux d'expérience, convenant aussi bien aux débutants qu'aux apprenants intermédiaires
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