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זהו קורס מבוא ממוקד שבוחן מהם מודלים גדולים של שפה (LLM), איך משתמשים בהם בתרחישים שונים לדוגמה ואיך אפשר לשפר את הביצועים שלהם באמצעות כוונון של הנחיות. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.
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What's inside
Syllabus
מבוא למודלים גדולים של שפה (LLM)
יחידת הלימוד הזו בוחנת מהם מודלים גדולים של שפה (LLM), איך משתמשים בהם בתרחישים שונים לדוגמה ואיך אפשר לשפר את הביצועים שלהם באמצעות כוונון של הנחיות. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with no prior experience in the topic of Large Language Models (LLMs)
Taught by Google Cloud Training, a recognized authority in the field of cloud computing which includes expertise in LLM
Covers essential concepts in the field of LLM and provides practical examples of their applications
Includes practical guidance on how to develop applications powered by LLM
Save this course
Save Introduction to Large Language Models - בעברית 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 Introduction to Large Language Models - בעברית with these
activities:
Re-familiarize yourself with NLP and ML Fundamentals
Show steps
Refresh your foundational knowledge of NLP and ML to strengthen your preparation for the course materials.
Browse courses on
NLP
Show steps
-
Review key NLP concepts such as tokenization, stemming, and lemmatization.
-
Revise fundamental ML algorithms like linear regression and decision trees.
Organize Course Resources and Notes
Show steps
Maintain a well-organized collection of your course materials to facilitate efficient review and recall.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Regularly update your notes and add relevant resources such as articles, videos, and assignments.
-
Consider using tools like Evernote or Notion for easy organization and access.
Read "Deep Learning" by Ian Goodfellow et al.
Show steps
Gain a deeper understanding of the theoretical foundations of LLMs from a comprehensive resource.
View
Deep Learning
on Amazon
Show steps
-
Study the chapters on neural networks and deep learning architectures.
-
Focus on the sections covering transformers and attention mechanisms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize a Study Group with Peers
Show steps
Collaborate with your peers to enhance your understanding through group discussions and knowledge sharing.
Show steps
-
Form a study group with other students taking the course.
-
Set regular meeting times and discuss key concepts, assignments, and projects.
Complete TensorFlow Tutorials on LLM Applications
Show steps
Expand your practical knowledge of LLMs by working through guided tutorials.
Browse courses on
TensorFlow
Show steps
-
Follow the TensorFlow tutorial on building a language model.
-
Explore tutorials on using LLMs for text generation and translation.
Build a Simple Text Summarizer
Show steps
Develop a hands-on project to apply your understanding of NLP and reinforce your learning.
Browse courses on
Text Summarization
Show steps
-
Choose a dataset of text documents.
-
Implement a text summarization algorithm using Python or R.
-
Evaluate the performance of your summarizer.
Contribute to Open-Source LLM Projects
Show steps
Gain practical experience and contribute to the broader LLM community by participating in open-source projects.
Show steps
-
Identify reputable open-source LLM projects on platforms like GitHub.
-
Review the project documentation and identify areas where you can contribute.
-
Submit bug fixes, feature enhancements, or documentation improvements.
Develop an LLM-Powered Chatbot
Show steps
Challenge yourself with a substantial project that integrates LLMs with practical applications.
Show steps
-
Design the architecture and user interface for your chatbot.
-
Implement the LLM integration and train the model on a relevant dataset.
-
Deploy and evaluate your chatbot, collecting feedback for improvements.
Re-familiarize yourself with NLP and ML Fundamentals
Show steps
Refresh your foundational knowledge of NLP and ML to strengthen your preparation for the course materials.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Review key NLP concepts such as tokenization, stemming, and lemmatization.
- Revise fundamental ML algorithms like linear regression and decision trees.
Organize Course Resources and Notes
Show steps
Maintain a well-organized collection of your course materials to facilitate efficient review and recall.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Regularly update your notes and add relevant resources such as articles, videos, and assignments.
- Consider using tools like Evernote or Notion for easy organization and access.
Read "Deep Learning" by Ian Goodfellow et al.
Show steps
Gain a deeper understanding of the theoretical foundations of LLMs from a comprehensive resource.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Study the chapters on neural networks and deep learning architectures.
- Focus on the sections covering transformers and attention mechanisms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize a Study Group with Peers
Show steps
Collaborate with your peers to enhance your understanding through group discussions and knowledge sharing.
Show steps
Show steps
Show steps
- Form a study group with other students taking the course.
- Set regular meeting times and discuss key concepts, assignments, and projects.
Complete TensorFlow Tutorials on LLM Applications
Show steps
Expand your practical knowledge of LLMs by working through guided tutorials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the TensorFlow tutorial on building a language model.
- Explore tutorials on using LLMs for text generation and translation.
Build a Simple Text Summarizer
Show steps
Develop a hands-on project to apply your understanding of NLP and reinforce your learning.
Browse courses on
Text Summarization
Show steps
Show steps
Show steps
- Choose a dataset of text documents.
- Implement a text summarization algorithm using Python or R.
- Evaluate the performance of your summarizer.
Contribute to Open-Source LLM Projects
Show steps
Gain practical experience and contribute to the broader LLM community by participating in open-source projects.
Show steps
Show steps
Show steps
- Identify reputable open-source LLM projects on platforms like GitHub.
- Review the project documentation and identify areas where you can contribute.
- Submit bug fixes, feature enhancements, or documentation improvements.
Develop an LLM-Powered Chatbot
Show steps
Challenge yourself with a substantial project that integrates LLMs with practical applications.
Show steps
Show steps
Show steps
- Design the architecture and user interface for your chatbot.
- Implement the LLM integration and train the model on a relevant dataset.
- Deploy and evaluate your chatbot, collecting feedback for improvements.
Career center
Learners who complete Introduction to Large Language Models - בעברית will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing (NLP) Engineer
Natural Language Processing (NLP) Engineer
An NLP Engineer builds and deploys NLP models. This course may be useful as it discusses how to use and improve the performance of LLMs, which are a type of NLP model. This course also covers the basics of LLMs and how they are used in different applications.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys ML models. This course may be useful as it discusses how to use and improve the performance of LLMs, which are a type of ML model. This course also covers the basics of LLMs and how they are used in different applications.
Data Scientist
Data Scientist
A Data Scientist builds machine learning (ML) models that can make predictions and generate insights from data. This course may be useful as it discusses how to use and improve the performance of LLMs, which are a type of ML model. This course also covers the basics of LLMs and how they are used in different applications.
Data Analyst
Data Analyst
A Data Analyst analyzes data to identify trends and patterns. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for analysts who are working with text data. This course also covers the basics of LLMs and how they can be used in different applications.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes to identify areas for improvement. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for analysts who are working with text data. This course also covers the basics of LLMs and how they can be used in different applications.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be helpful as it provides an overview of LLMs and how they are used to develop generative AI applications. This course also covers the basics of LLMs and how they are used in different applications, which can be beneficial for Software Engineers who are working on AI projects.
Product Manager
Product Manager
A Product Manager defines and manages the roadmap for a product. This course may be helpful as it provides an overview of LLMs and their capabilities, which can be beneficial for Product Managers who are working on AI-powered products. This course also covers the basics of LLMs and how they can be used in different applications.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Marketing Managers who are working on AI-powered marketing campaigns. This course also covers the basics of LLMs and how they can be used in different applications.
Sales Manager
Sales Manager
A Sales Manager leads and manages a sales team. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Sales Managers who are working with AI-powered sales tools. This course also covers the basics of LLMs and how they can be used in different applications.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps customers get value from a product or service. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Customer Success Managers who are working with AI-powered customer support tools. This course also covers the basics of LLMs and how they can be used in different applications.
Content Strategist
Content Strategist
A Content Strategist develops and executes content strategies. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Content Strategists who are working with AI-powered content creation tools. This course also covers the basics of LLMs and how they can be used in different applications.
Technical Writer
Technical Writer
A Technical Writer creates documentation for technical products and services. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Technical Writers who are working with AI-powered writing tools. This course also covers the basics of LLMs and how they can be used in different applications.
Interaction Designer
Interaction Designer
An Interaction Designer designs and evaluates the user interaction of products and services. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Interaction Designers who are working with AI-powered user interaction design tools. This course also covers the basics of LLMs and how they can be used in different applications.
User Experience (UX) Designer
User Experience (UX) Designer
A UX Designer designs and evaluates the user experience of products and services. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for UX Designers who are working with AI-powered user experience research tools. This course also covers the basics of LLMs and how they can be used in different applications.
Information Architect
Information Architect
An Information Architect designs and organizes the structure of information on websites and other digital products. This course may be useful as it provides an overview of LLMs and their capabilities, which can be beneficial for Information Architects who are working with AI-powered information architecture tools. This course also covers the basics of LLMs and how they can be used in different applications.
For more career information including salaries, visit:
OpenCourser.com/course/x7scdg/introduction
Reading list
We've selected nine 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 - בעברית.
Provides a comprehensive overview of deep learning, including its history, architecture, and applications. It valuable resource for anyone who wants to learn more about deep learning.
This classic textbook provides a comprehensive overview of artificial intelligence, including machine learning and natural language processing. It valuable resource for anyone who wants to learn more about the foundations of AI.
Provides a comprehensive overview of natural language processing (NLP) techniques. It valuable resource for anyone who wants to learn more about NLP.
Provides a practical guide to machine learning, including its history, architecture, and applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a practical guide to machine learning for developers. It valuable resource for anyone who wants to learn more about how to use machine learning to solve real-world problems.
Provides a comprehensive overview of reinforcement learning, including its history, architecture, and applications. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a concise overview of machine learning, including its history, architecture, and applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Save
This textbook provides a comprehensive overview of deep learning, including its history, architecture, and applications. It valuable resource for anyone who wants to learn more about deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x7scdg/introduction
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Good to know
Suitable for learners with no prior experience in the topic of Large Language Models (LLMs)
Taught by Google Cloud Training, a recognized authority in the field of cloud computing which includes expertise in LLM
Covers essential concepts in the field of LLM and provides practical examples of their applications
Includes practical guidance on how to develop applications powered by LLM
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