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Reza Moradinezhad
This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.
Read more
This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.
Read more
This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.
This course is ideal for AI Product Managers looking to optimize LLM applications, Data Scientists interested in advanced AI model evaluation techniques, AI Ethicists and Policy Makers focused on responsible AI deployment, and Academic Researchers studying the impact of generative AI across various domains.
A basic understanding of artificial intelligence, machine learning concepts, and familiarity with natural language processing (NLP) is recommended. Prior experience with Google Cloud Vertex AI is beneficial but not required.
It covers practical applications, integrating human judgment with automatic methods, and prepares learners for future trends in AI evaluation across various media, including text, images, and audio. This comprehensive approach ensures you are equipped to assess LLMs effectively, enhancing business strategies and innovation.
Register for this course and see more details by visiting:
OpenCourser.com/course/gkhaoc/evaluating
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Syllabus
Evaluating Large Language Model Outputs: A Practical Guide
This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those wanting to optimize LLM applications, this course aligns well with the needs of AI Product Managers
Data Scientists seeking advanced AI model evaluation techniques will find this course valuable
AI Ethicists and Policy Makers concerned with responsible AI deployment will benefit from this course
Academic Researchers studying the impact of generative AI will find this course relevant
This course may not be suitable for beginners in AI, as it assumes a basic understanding of AI and machine learning concepts
Save this course
Save Evaluating Large Language Model Outputs: A Practical Guide to your list so you can find it easily later:
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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 Evaluating Large Language Model Outputs: A Practical Guide with these
activities:
Participate in Study Groups
Show steps
Study groups provide a collaborative and supportive environment that encourages knowledge exchange, solidifies understanding, and enhances problem-solving abilities.
Browse courses on
Collaborative Learning
Show steps
-
Form or join a study group with peers enrolled in the same course.
-
Meet regularly to discuss course materials, share insights, and work through assignments together.
Review Large Language Model (LLM) concepts
Show steps
Reviewing LLM concepts will ensure a solid foundation for the course, enabling you to grasp advanced evaluation techniques more effectively.
Browse courses on
Large Language Models
Show steps
-
Review foundational concepts of LLMs, such as transformer architecture and self-attention mechanisms.
-
Explore different types of LLMs, their capabilities, and limitations.
Review Pre-requisite Concepts
Show steps
Reviewing pre-requisite knowledge will reinforce foundations and ensure readiness to absorb and build on more advanced concepts in the course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Revisit foundational concepts in machine learning, such as supervised and unsupervised learning.
-
Brush up on deep learning architectures, including convolutional neural networks and recurrent neural networks.
-
Review key NLP concepts such as tokenization, stemming, and named entity recognition.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Connect with AI Practitioners
Show steps
Mentorship provides guidance, support, and exposure to industry insights, enhancing learners' professional growth in AI.
Browse courses on
Professional Development
Show steps
-
Identify potential mentors in the field of AI, particularly those with expertise in LLMs.
-
Reach out to potential mentors and express interest in connecting and learning from their experiences.
-
Schedule regular meetings or virtual coffee chats to discuss LLM applications, career paths, and industry trends.
Review Probability and Statistics
Show steps
Review fundamental concepts of probability and statistics to strengthen foundational knowledge for LLM evaluation.
Show steps
-
Revisit probability concepts such as random variables, distributions, and conditional probability.
-
Review statistical concepts like mean, variance, hypothesis testing, and regression.
-
Solve practice problems to reinforce understanding.
Attend AI Conferences
Show steps
Attending AI conferences allows learners to connect with professionals in the field, learn about the latest trends and research, and explore potential career opportunities.
Show steps
-
Identify industry conferences that focus on LLMs or generative AI.
-
Attend keynotes, breakout sessions, and workshops to gain insights from experts.
-
Network with other attendees to build relationships and explore collaborations.
Engage in discussions on emerging trends and challenges in LLM evaluation
Show steps
Engaging in discussions with peers will broaden your perspective on the latest advancements and challenges in LLM evaluation, fostering a deeper understanding.
Browse courses on
Emerging Trends
Show steps
-
Join online forums or discussion groups dedicated to LLM evaluation.
-
Participate in discussions, share your insights, and learn from others' experiences.
-
Stay informed about industry events and webinars on LLM evaluation.
Evaluate LLM outputs using automatic metrics
Show steps
Practicing evaluation techniques using automatic metrics will enhance your proficiency in assessing LLM outputs, preparing you for real-world scenarios.
Show steps
-
Familiarize yourself with different automatic metrics for LLM evaluation.
-
Apply automatic metrics to evaluate LLM outputs, such as text generation, translation, and question answering.
-
Analyze and interpret the results of automatic evaluation.
Explore Vertex AI's Text Tools
Show steps
Working through Vertex AI tutorials will enable learners to apply theoretical concepts by implementing practical machine learning tasks.
Browse courses on
Vertex AI
Show steps
-
Follow a tutorial to deploy a text classification model using Vertex AI's AutoML Natural Language.
-
Explore Vertex AI's Natural Language API for sentiment analysis and entity extraction.
Explore advanced LLM evaluation techniques with Vertex AI
Show steps
Following tutorials on advanced evaluation techniques using Vertex AI will deepen your understanding and expand your skillset for LLM evaluation.
Browse courses on
Vertex AI
Show steps
-
Identify the appropriate advanced evaluation techniques for your specific LLM application.
-
Follow guided tutorials to implement these techniques using Vertex AI's tools.
-
Experiment with different techniques to find the most effective approach for your needs.
Evaluate LLM Outputs
Show steps
Hands-on evaluation exercises will develop learners' critical thinking and analytical skills when assessing LLM performance.
Browse courses on
Performance Analysis
Show steps
-
Evaluate LLM outputs for tasks such as text generation, translation, and question answering.
-
Apply different evaluation metrics, such as BLEU and ROUGE, to measure the quality of LLM outputs.
-
Compare and contrast the performance of different LLMs on specific tasks.
参加人工智能伦理研讨会
Show steps
Engage in discussions and hands-on activities to develop a deeper understanding of ethical considerations and best practices in LLM deployment.
Browse courses on
AI Ethics
Show steps
-
Attend workshops organized by AI ethics organizations or research institutions.
-
Participate in group discussions and case studies.
-
Develop ethical guidelines and principles for AI projects.
Compile Resources on LLM Bias Mitigation
Show steps
Compiling resources will foster learners' understanding of ethical considerations and best practices in LLM development and deployment.
Browse courses on
Ethical considerations in AI
Show steps
-
Gather research papers, articles, and case studies on LLM bias mitigation techniques.
-
Organize the resources into categories, such as bias types, mitigation strategies, and evaluation methods.
-
Create a repository or documentation to share the compiled resources with others.
Create a case study on LLM evaluation in a specific industry or domain
Show steps
Developing a case study will allow you to apply your knowledge to a practical scenario, reinforcing your understanding of LLM evaluation in real-world contexts.
Browse courses on
Case study
Show steps
-
Choose a specific industry or domain where LLM evaluation is particularly relevant and impactful.
-
Research and gather data on LLM applications and evaluation methods used in that field.
-
Develop a comprehensive case study that showcases your findings and insights.
-
Present your case study to peers or industry professionals to gather feedback and expand your knowledge.
Participate in AI Challenges
Show steps
Participating in AI challenges provides practical experience, pushes learners to explore innovative solutions, and fosters a competitive spirit.
Show steps
-
Identify AI challenges or hackathons related to LLMs or generative AI.
-
Form a team or work individually to develop and submit solutions.
-
Receive feedback on submissions and learn from the solutions of others.
Participate in Study Groups
Show steps
Study groups provide a collaborative and supportive environment that encourages knowledge exchange, solidifies understanding, and enhances problem-solving abilities.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Form or join a study group with peers enrolled in the same course.
- Meet regularly to discuss course materials, share insights, and work through assignments together.
Review Large Language Model (LLM) concepts
Show steps
Reviewing LLM concepts will ensure a solid foundation for the course, enabling you to grasp advanced evaluation techniques more effectively.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Review foundational concepts of LLMs, such as transformer architecture and self-attention mechanisms.
- Explore different types of LLMs, their capabilities, and limitations.
Review Pre-requisite Concepts
Show steps
Reviewing pre-requisite knowledge will reinforce foundations and ensure readiness to absorb and build on more advanced concepts in the course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Revisit foundational concepts in machine learning, such as supervised and unsupervised learning.
- Brush up on deep learning architectures, including convolutional neural networks and recurrent neural networks.
- Review key NLP concepts such as tokenization, stemming, and named entity recognition.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Connect with AI Practitioners
Show steps
Mentorship provides guidance, support, and exposure to industry insights, enhancing learners' professional growth in AI.
Browse courses on
Professional Development
Show steps
Show steps
Show steps
- Identify potential mentors in the field of AI, particularly those with expertise in LLMs.
- Reach out to potential mentors and express interest in connecting and learning from their experiences.
- Schedule regular meetings or virtual coffee chats to discuss LLM applications, career paths, and industry trends.
Review Probability and Statistics
Show steps
Review fundamental concepts of probability and statistics to strengthen foundational knowledge for LLM evaluation.
Show steps
Show steps
Show steps
- Revisit probability concepts such as random variables, distributions, and conditional probability.
- Review statistical concepts like mean, variance, hypothesis testing, and regression.
- Solve practice problems to reinforce understanding.
Attend AI Conferences
Show steps
Attending AI conferences allows learners to connect with professionals in the field, learn about the latest trends and research, and explore potential career opportunities.
Show steps
Show steps
Show steps
- Identify industry conferences that focus on LLMs or generative AI.
- Attend keynotes, breakout sessions, and workshops to gain insights from experts.
- Network with other attendees to build relationships and explore collaborations.
Engage in discussions on emerging trends and challenges in LLM evaluation
Show steps
Engaging in discussions with peers will broaden your perspective on the latest advancements and challenges in LLM evaluation, fostering a deeper understanding.
Browse courses on
Emerging Trends
Show steps
Show steps
Show steps
- Join online forums or discussion groups dedicated to LLM evaluation.
- Participate in discussions, share your insights, and learn from others' experiences.
- Stay informed about industry events and webinars on LLM evaluation.
Evaluate LLM outputs using automatic metrics
Show steps
Practicing evaluation techniques using automatic metrics will enhance your proficiency in assessing LLM outputs, preparing you for real-world scenarios.
Show steps
Show steps
Show steps
- Familiarize yourself with different automatic metrics for LLM evaluation.
- Apply automatic metrics to evaluate LLM outputs, such as text generation, translation, and question answering.
- Analyze and interpret the results of automatic evaluation.
Explore Vertex AI's Text Tools
Show steps
Working through Vertex AI tutorials will enable learners to apply theoretical concepts by implementing practical machine learning tasks.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Follow a tutorial to deploy a text classification model using Vertex AI's AutoML Natural Language.
- Explore Vertex AI's Natural Language API for sentiment analysis and entity extraction.
Explore advanced LLM evaluation techniques with Vertex AI
Show steps
Following tutorials on advanced evaluation techniques using Vertex AI will deepen your understanding and expand your skillset for LLM evaluation.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Identify the appropriate advanced evaluation techniques for your specific LLM application.
- Follow guided tutorials to implement these techniques using Vertex AI's tools.
- Experiment with different techniques to find the most effective approach for your needs.
Evaluate LLM Outputs
Show steps
Hands-on evaluation exercises will develop learners' critical thinking and analytical skills when assessing LLM performance.
Browse courses on
Performance Analysis
Show steps
Show steps
Show steps
- Evaluate LLM outputs for tasks such as text generation, translation, and question answering.
- Apply different evaluation metrics, such as BLEU and ROUGE, to measure the quality of LLM outputs.
- Compare and contrast the performance of different LLMs on specific tasks.
参加人工智能伦理研讨会
Show steps
Engage in discussions and hands-on activities to develop a deeper understanding of ethical considerations and best practices in LLM deployment.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Attend workshops organized by AI ethics organizations or research institutions.
- Participate in group discussions and case studies.
- Develop ethical guidelines and principles for AI projects.
Compile Resources on LLM Bias Mitigation
Show steps
Compiling resources will foster learners' understanding of ethical considerations and best practices in LLM development and deployment.
Browse courses on
Ethical considerations in AI
Show steps
Show steps
Show steps
- Gather research papers, articles, and case studies on LLM bias mitigation techniques.
- Organize the resources into categories, such as bias types, mitigation strategies, and evaluation methods.
- Create a repository or documentation to share the compiled resources with others.
Create a case study on LLM evaluation in a specific industry or domain
Show steps
Developing a case study will allow you to apply your knowledge to a practical scenario, reinforcing your understanding of LLM evaluation in real-world contexts.
Browse courses on
Case study
Show steps
Show steps
Show steps
- Choose a specific industry or domain where LLM evaluation is particularly relevant and impactful.
- Research and gather data on LLM applications and evaluation methods used in that field.
- Develop a comprehensive case study that showcases your findings and insights.
- Present your case study to peers or industry professionals to gather feedback and expand your knowledge.
Participate in AI Challenges
Show steps
Participating in AI challenges provides practical experience, pushes learners to explore innovative solutions, and fosters a competitive spirit.
Show steps
Show steps
Show steps
- Identify AI challenges or hackathons related to LLMs or generative AI.
- Form a team or work individually to develop and submit solutions.
- Receive feedback on submissions and learn from the solutions of others.
Career center
Learners who complete Evaluating Large Language Model Outputs: A Practical Guide will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/gkhaoc/evaluating
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Effort
1 Hour
Level
Introductory
Via
Coursera
Institution
Coursera Instructor Network
Instructor
Reza Moradinezhad
Session
August 12, 2024
- August 26, 2024
Language
English
Transcripts
Español
Français
Português
Deutsch
中文
العربية
Pусский
Bahasa Indonesia
Good to know
Suitable for those wanting to optimize LLM applications, this course aligns well with the needs of AI Product Managers
Data Scientists seeking advanced AI model evaluation techniques will find this course valuable
AI Ethicists and Policy Makers concerned with responsible AI deployment will benefit from this course
Academic Researchers studying the impact of generative AI will find this course relevant
This course may not be suitable for beginners in AI, as it assumes a basic understanding of AI and machine learning concepts
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