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Ce cours de micro-apprentissage, qui s'adresse aux débutants, explique ce que sont les grands modèles de langage (LLM). Il inclut des cas d'utilisation et décrit comment améliorer les performances des LLM grâce au réglage des invites. Il présente aussi les outils Google qui vous aideront à développer vos propres applications d'IA générative.
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Ce cours s'adresse aux débutants et explique les concepts de base des grands modèles de langage (LLM)
Il fournit des cas d'utilisation pratiques et des conseils pour améliorer les performances des LLM en ajustant les invites
Ce cours présente les outils Google qui permettent de développer des applications d'IA générative
Il s'agit d'un cours de micro-apprentissage, ce qui le rend accessible aux personnes disposant de peu de temps
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Reviews summary
Introduction rapide et claire aux llm
Selon les apprenants, ce cours est une excellente introduction aux grands modèles de langage (LLM), claire et concise, idéale pour les débutants et les professionnels pressés. Le format micro-apprentissage est particulièrement apprécié pour son efficacité. Il fournit un bon aperçu des capacités des LLM et met l'accent sur les applications pratiques et les outils Google Cloud, ce qui est très pertinent pour le travail. Certains étudiants, cependant, trouvent que le contenu manque de profondeur technique et souhaiteraient plus d'exercices pratiques pour aller au-delà des bases. Il est considéré comme un solide point de départ pour explorer l'IA générative.
Met en avant les applications pratiques et les outils Google Cloud.
"J'ai particulièrement apprécié la présentation des outils Google Cloud."
"J'apprécie la façon dont le cours met l'accent sur les applications pratiques et les produits Google, ce qui est très pertinent pour mon travail."
"Les démonstrations avec les outils Google étaient pertinentes."
Un excellent point de départ pour comprendre les bases des LLM.
"Parfait pour les débutants."
"C'était un bon aperçu des capacités des modèles de langage et comment commencer à les utiliser."
"Un excellent tremplin pour des études plus avancées en IA générative."
"Ce cours m'a donné l'envie d'explorer davantage les LLM."
Un cours très clair et facile à comprendre pour les débutants.
"Ce cours est une excellente introduction aux LLM. Les explications sont très claires et concises..."
"Fantastique pour comprendre rapidement ce que sont les LLM."
"Clair et accessible, ce cours remplit sa promesse d'introduction. Les explications sont faciles à suivre..."
"Il démystifie bien les LLM et leurs applications."
Peu d'exercices interactifs ou d'exemples de code concrets.
"Manque d'exercices pratiques."
"J'aurais aimé quelques exemples de code ou des exercices plus concrets pour vraiment 'mettre les mains dans le cambouis'."
"Seul bémol, le manque d'activités interactives pour tester la compréhension."
Trop basique pour ceux ayant déjà une connaissance des LLM.
"J'ai trouvé le cours trop introductif. Il ne va pas assez en profondeur et les explications sont trop simplifiées."
"Pour quelqu'un qui a déjà une vague idée des LLM, ce n'est pas très enrichissant."
"Si vous avez déjà lu quelques articles sur les LLM, vous n'apprendrez pas grand-chose."
"Déçu par le manque de profondeur."
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 Introduction to Large Language Models - Français with these
activities:
Review LLM concepts
Show steps
Refreshes LLM concepts for a smoother learning experience in this course.
Browse courses on
LLM
Show steps
-
Define LLMs and their capabilities
-
Recall different types of LLMs
Create a comprehensive study guide
Show steps
Enhances retention and knowledge organization by compiling a structured study guide.
Show steps
-
Review course materials and identify key concepts
-
Summarize and organize information in a coherent manner
Seek guidance from an experienced LLM practitioner
Show steps
Provides access to valuable insights and guidance from experts in the field.
Show steps
-
Identify potential mentors through networking events or online platforms
-
Reach out and request mentorship
Five other activities
Expand to see all activities and additional details
Show all eight activities
Assist fellow learners through Q&A
Show steps
Strengthens understanding of LLM concepts by explaining them to others and addressing their queries.
Show steps
-
Engage in discussion forums or Q&A platforms
-
Provide clear and informative responses to fellow learners
Explore Google's LLM development tools
Show steps
Introduces Google's LLM development tools to facilitate practical experience.
Browse courses on
Google AI Platform
Show steps
-
Follow tutorials on using Google's LLM development tools
-
Experiment with the tools to gain hands-on experience
Fine-tune LLM prompts
Show steps
Provides hands-on practice to enhance proficiency in prompt engineering for LLMs.
Browse courses on
Prompt Engineering
Show steps
-
Experiment with different prompt formats
-
Analyze LLM responses and refine prompts accordingly
Attend an LLM industry conference
Show steps
Provides opportunities to connect with professionals and learn about industry best practices.
Browse courses on
Networking
Show steps
-
Research and identify relevant industry conferences
-
Attend the conference and actively participate in networking sessions
Develop an LLM-powered application
Show steps
Encourages practical application of LLM knowledge through project-based learning.
Browse courses on
LLM Applications
Show steps
-
Identify a problem or opportunity that an LLM can address
-
Design and develop an LLM-powered application
Review LLM concepts
Show steps
Refreshes LLM concepts for a smoother learning experience in this course.
Browse courses on
LLM
Show steps
Show steps
Show steps
- Define LLMs and their capabilities
- Recall different types of LLMs
Create a comprehensive study guide
Show steps
Enhances retention and knowledge organization by compiling a structured study guide.
Show steps
Show steps
Show steps
- Review course materials and identify key concepts
- Summarize and organize information in a coherent manner
Seek guidance from an experienced LLM practitioner
Show steps
Provides access to valuable insights and guidance from experts in the field.
Show steps
Show steps
Show steps
- Identify potential mentors through networking events or online platforms
- Reach out and request mentorship
Five other activities
Expand to see all activities and additional details
Show all eight activities
Assist fellow learners through Q&A
Show steps
Strengthens understanding of LLM concepts by explaining them to others and addressing their queries.
Show steps
Show steps
Show steps
- Engage in discussion forums or Q&A platforms
- Provide clear and informative responses to fellow learners
Explore Google's LLM development tools
Show steps
Introduces Google's LLM development tools to facilitate practical experience.
Browse courses on
Google AI Platform
Show steps
Show steps
Show steps
- Follow tutorials on using Google's LLM development tools
- Experiment with the tools to gain hands-on experience
Fine-tune LLM prompts
Show steps
Provides hands-on practice to enhance proficiency in prompt engineering for LLMs.
Browse courses on
Prompt Engineering
Show steps
Show steps
Show steps
- Experiment with different prompt formats
- Analyze LLM responses and refine prompts accordingly
Attend an LLM industry conference
Show steps
Provides opportunities to connect with professionals and learn about industry best practices.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research and identify relevant industry conferences
- Attend the conference and actively participate in networking sessions
Develop an LLM-powered application
Show steps
Encourages practical application of LLM knowledge through project-based learning.
Browse courses on
LLM Applications
Show steps
Show steps
Show steps
- Identify a problem or opportunity that an LLM can address
- Design and develop an LLM-powered application
Career center
Learners who complete Introduction to Large Language Models - Français will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist can take this Introduction to Large Language Models - Français course to help build a foundation for their work in developing and using artificial intelligence and machine learning models. This course will provide a comprehensive overview of the theory and practice of machine learning, with a focus on deep learning and natural language processing. The course will cover a variety of topics, including data preprocessing, feature engineering, model selection, and model evaluation. Additionally, this course will provide students with the opportunity to gain hands-on experience with machine learning tools and techniques by completing a series of coding assignments.
Machine Learning Engineer
Machine Learning Engineer
Similarly, this Introduction to Large Language Models - Français course may be of use to a Machine Learning Engineer as they work with machine learning models. This course will provide a broad overview of machine learning, from its theoretical foundations to its practical applications. Students will learn about different types of machine learning algorithms, how to train and evaluate models, and how to apply machine learning to real-world problems. This course may be especially helpful for Machine Learning Engineers who are new to the field or who want to learn more about deep learning and natural language processing.
Software Engineer
Software Engineer
This Introduction to Large Language Models - Français course could be useful for a Software Engineer looking to work on AI-driven projects. This course will introduce students to the basics of artificial intelligence, from its history and development to its potential applications in various industries. Students will learn about different types of AI algorithms, how to develop and train AI models, and how to evaluate their performance.
Data Analyst
Data Analyst
A Data Analyst may find this Introduction to Large Language Models - Français course helpful as they work with data to solve business problems. This course will introduce fundamental concepts in statistics and probability, with an emphasis on data analysis. Students will learn how to collect, clean, and analyze data, and how to communicate their findings effectively.
Business Analyst
Business Analyst
For a Business Analyst looking to use AI and data to improve business processes and outcomes, this Introduction to Large Language Models - Français course may be useful. This course will provide a comprehensive overview of the field of business analytics, including data analysis, data mining, and predictive modeling. Students will learn how to use data to identify business opportunities, solve problems, and make better decisions.
Product Manager
Product Manager
A Product Manager who wants to build AI-powered products might find this Introduction to Large Language Models - Français course useful. This course will teach students the fundamentals of product management, including product development, market research, and user experience design. Students will learn how to define product requirements, create product roadmaps, and launch new products.
Marketing Manager
Marketing Manager
A Marketing Manager interested in using artificial intelligence to improve marketing campaigns may find this Introduction to Large Language Models - Français course helpful. This course will provide a comprehensive overview of the field of marketing, including marketing strategy, market research, and digital marketing. Students will learn how to develop and execute marketing campaigns, and how to use data to measure their effectiveness.
Sales Manager
Sales Manager
A Sales Manager who wants to learn how to use AI to improve their sales process may find this Introduction to Large Language Models - Français course helpful. This course will teach students the fundamentals of sales management, including sales strategy, sales process, and customer relationship management. Students will learn how to build and manage a sales team, and how to use data to improve their sales performance.
Customer Success Manager
Customer Success Manager
For a Customer Success Manager looking to leverage data to improve customer satisfaction and retention, this Introduction to Large Language Models - Français course may be useful. This course will teach students the fundamentals of customer success management, including customer onboarding, customer support, and customer churn management. Students will learn how to build and manage a customer success team, and how to use data to improve their customer success rate.
Operations Manager
Operations Manager
An Operations Manager who wants to improve the efficiency of their operations may find this Introduction to Large Language Models - Français course useful. This course will teach students the fundamentals of operations management, including process improvement, inventory management, and supply chain management. Students will learn how to design and implement operational processes, and how to use data to improve their operational performance.
Project Manager
Project Manager
A Project Manager interested in using AI to improve project outcomes could benefit from this Introduction to Large Language Models - Français course. This course will teach students the fundamentals of project management, including project planning, project execution, and project control. Students will learn how to define project scope, develop project plans, and manage project risks.
Risk Manager
Risk Manager
For a Risk Manager looking to use AI to identify and manage risks, this Introduction to Large Language Models - Français course may be helpful. This course will teach students the fundamentals of risk management, including risk identification, risk assessment, and risk mitigation. Students will learn how to develop and implement risk management plans, and how to use data to improve their risk management practices.
Compliance Manager
Compliance Manager
A Compliance Manager who wants to use AI to ensure compliance with regulations may find this Introduction to Large Language Models - Français course useful. This course will teach students the fundamentals of compliance management, including compliance auditing, compliance monitoring, and compliance reporting. Students will learn how to develop and implement compliance programs, and how to use data to improve their compliance performance.
Quality Assurance Manager
Quality Assurance Manager
For a Quality Assurance Manager interested in using AI to improve the quality of their products or services, this Introduction to Large Language Models - Français course may be useful. This course will teach students the fundamentals of quality assurance, including quality planning, quality control, and quality improvement. Students will learn how to develop and implement quality assurance programs, and how to use data to improve their quality assurance practices.
Information Security Manager
Information Security Manager
An Information Security Manager who wants to use AI to improve the security of their organization's data and systems may find this Introduction to Large Language Models - Français course useful. This course will teach students the fundamentals of information security, including information security risk assessment, information security controls, and information security incident management. Students will learn how to develop and implement information security programs, and how to use data to improve their information security practices.
For more career information including salaries, visit:
OpenCourser.com/course/a4yks2/introduction
Reading list
We've selected 11 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
Introduction to Large Language Models - Français.
Provides a comprehensive overview of language models, from basic concepts to advanced techniques. Useful for gaining a deeper understanding of the theoretical foundations of LLM.
Provides a comprehensive overview of information theory, inference, and learning algorithms, which are fundamental concepts for understanding and developing machine learning models.
Provides a comprehensive overview of deep learning with Python. Useful for gaining a deeper understanding of the underlying algorithms used in LLM.
Provides a comprehensive overview of the field of natural language processing, covering topics such as language modeling, machine translation, and question answering.
Provides a comprehensive overview of reinforcement learning, which type of machine learning that allows agents to learn how to behave in an environment by interacting with it.
Offers practical guidance on building and deploying NLP applications. Useful for learners who want to apply their knowledge of LLM in real-world projects.
Provides a comprehensive overview of statistical methods for machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
Covers the fundamentals of speech and language processing, including natural language understanding and generation. Useful for gaining a broader perspective on NLP and its applications.
Provides a comprehensive overview of NLP, including its history, techniques, and applications. Useful for gaining a broader perspective on NLP and its applications.
Provides a comprehensive overview of the mathematical foundations of machine learning, covering topics such as probability, statistics, and optimization.
Provides a clear and concise introduction to linear algebra, which fundamental mathematical tool for many areas of science and engineering, including machine learning and natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a4yks2/introduction
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Effort
1 hour to complete
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Transcript
English
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