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Mary Ellen Foster,
Sean MacAvaney,
and
Jake Lever
Large language models such as GPT-3.5, which powers ChatGPT, are changing how humans interact with computers and how computers can process text. This course will introduce the fundamental ideas of natural language processing and language modelling that underpin these large language models. We will explore the basics of how language models work, and the specifics of how newer neural-based approaches are built. We will examine the key innovations that have enabled Transformer-based large language models to become dominant in solving various language tasks. Finally, we will examine the challenges in applying these large language models to various problems including the ethical problems involved in their construction and use.
Read more
Large language models such as GPT-3.5, which powers ChatGPT, are changing how humans interact with computers and how computers can process text. This course will introduce the fundamental ideas of natural language processing and language modelling that underpin these large language models. We will explore the basics of how language models work, and the specifics of how newer neural-based approaches are built. We will examine the key innovations that have enabled Transformer-based large language models to become dominant in solving various language tasks. Finally, we will examine the challenges in applying these large language models to various problems including the ethical problems involved in their construction and use.
Read more
Large language models such as GPT-3.5, which powers ChatGPT, are changing how humans interact with computers and how computers can process text. This course will introduce the fundamental ideas of natural language processing and language modelling that underpin these large language models. We will explore the basics of how language models work, and the specifics of how newer neural-based approaches are built. We will examine the key innovations that have enabled Transformer-based large language models to become dominant in solving various language tasks. Finally, we will examine the challenges in applying these large language models to various problems including the ethical problems involved in their construction and use.
Through hands-on labs, we will learn about the building blocks of Transformers and apply them for generating new text. These Python exercises step you through the process of applying a smaller language model and understanding how it can be evaluated and applied to various problems. Regular practice quizzes will help reinforce the knowledge and prepare you for the graded assessments.
Register for this course and see more details by visiting:
OpenCourser.com/course/8fy8by/generative
What's inside
Syllabus
Language Modeling
This module introduces the concept of language modelling, which is the foundation of models like GPT.
Transformers and GPT
This module describes the technical background for neural language models and an overview of how they are used to generate text.
Read more
Syllabus
Language Modeling
This module introduces the concept of language modelling, which is the foundation of models like GPT.
Transformers and GPT
This module describes the technical background for neural language models and an overview of how they are used to generate text.
Read more
Applications and Implications
This module discusses considerations that are necessary when using GPT and similar models in real-world contexts, specifically discussing the risks of using these models and approaches to mitigating these risks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores deep learning models, particularly GPT-3.5, which are rapidly shaping numerous industries
Introduces the basics of language modelling and its relevance in understanding and generating text
Taught by seasoned experts, including Mary Ellen Foster, Sean MacAvaney, and Jake Lever
Provides hands-on labs and interactive materials for practical application of concepts
Suitable for individuals seeking a strong foundation in natural language processing and language modelling
Covers the key innovations that have enabled Transformer-based large language models to excel
Save this course
Save Generative Pre-trained Transformers (GPT) 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 Generative Pre-trained Transformers (GPT) with these
activities:
Review Python basics
Show steps
Solidify your understanding of the syntax and fundamentals of Python.
Browse courses on
Python
Show steps
-
Review online tutorials
-
Refresh your memory on the official Python documentation.
-
Solve basic coding problems using online platforms.
Review natural language processing fundamentals
Show steps
Strengthen the foundation by revisiting core concepts in natural language processing, as they are essential for understanding large language models.
Browse courses on
Natural Language Processing
Show steps
-
Go through lecture notes or textbooks on topics like tokenization, stemming, and part-of-speech tagging.
-
Practice solving problems related to text preprocessing and feature extraction.
Recall the concept of natural language processing
Show steps
Enhance your understanding of the fundamental principles of natural language processing techniques.
Browse courses on
Natural Language Processing
Show steps
-
Revise your notes from previous courses or online resources.
-
Explore introductory materials and tutorials on NLP.
-
Participate in online forums or discussion groups on NLP.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Create a knowledge base
Show steps
Improve retention and organization by compiling notes, assignments, and other course materials into a comprehensive knowledge base.
Show steps
-
Gather and organize lecture notes, slides, and handouts.
-
Summarize key concepts and important ideas.
-
Add additional resources, such as relevant articles and videos, to complement the materials.
Review basics of NLP
Show steps
Reviewing basic concepts in NLP will help you acclimate to the technical terminology and ideas used in this course.
Browse courses on
Natural Language Processing
Show steps
-
Review foundational concepts of NLP, including tokenization, stemming, and part-of-speech tagging.
-
Review different types of NLP tasks, such as text classification, named entity recognition, and machine translation.
-
Identify and understand key algorithms and techniques used in NLP, such as TF-IDF, word embeddings, and neural networks.
Join Peer Discussion Groups
Show steps
Engage with fellow learners by participating in discussion groups, exchanging insights, and providing support, which can enhance your understanding of the course topics.
Browse courses on
Discussion
Show steps
-
Identify active discussion forums or create your own group.
-
Participate in discussions by sharing your perspectives, asking questions, and responding to others.
-
Actively listen to diverse viewpoints and engage in respectful discussions.
-
Collaborate on projects or assignments to gain different perspectives and improve your teamwork skills.
Engage in discussion forums
Show steps
Promote active learning by participating in discussions with peers, exchanging insights, and clarifying concepts related to large language models.
Show steps
-
Participate in online forums dedicated to natural language processing and machine learning.
-
Ask questions, share ideas, and engage with other learners to enhance understanding.
Practice coding exercises
Show steps
Improve understanding of foundational concepts through hands-on exercises related to language models and their programming framework.
Show steps
-
Solve hands-on coding problems on platforms like LeetCode or HackerRank
-
Implement various types of language models, such as LSTM, GRU, and Transformer, in Python.
-
Experiment with different hyperparameters and training datasets to optimize model performance.
Practice Python coding for NLP tasks
Show steps
Practicing Python coding will help you develop the skills necessary to implement and experiment with NLP algorithms.
Browse courses on
Python Programming
Show steps
-
Solve coding exercises on Python libraries for NLP, such as NLTK or spaCy.
-
Implement NLP algorithms from scratch, such as a bag-of-words model or a simple neural network for text classification.
Explore advanced Transformer architectures
Show steps
Enhance understanding of the theoretical foundations and architectural advancements in Transformer models.
Show steps
-
Follow research papers and blog posts on topics such as self-attention, encoder-decoder architectures, and pre-training techniques.
-
Attend webinars or workshops conducted by experts in the field.
Practice text generation with pre-trained models
Show steps
Develop your practical skills in using pre-trained models for text-based tasks.
Browse courses on
Text Generation
Show steps
-
Identify a pre-trained language model such as GPT-3 or similar.
-
Access the model's API or interface.
-
Experiment with different prompts and parameters to generate text.
-
Analyze the generated text for quality and relevance.
Build a simple text classification model
Show steps
Building a text classification model will allow you to apply the concepts and techniques learned in the course to a practical problem.
Browse courses on
Text Classification
Show steps
-
Choose a dataset for text classification.
-
Preprocess the data, including tokenization, stemming, and creating feature vectors.
-
Train a machine learning model for text classification, such as logistic regression or a neural network.
-
Evaluate the performance of the model using metrics such as accuracy and F1 score.
Complete Practice Problems
Show steps
Sharpen your understanding of the fundamental concepts and techniques discussed in the course by working through practice problems.
Browse courses on
Language Modeling
Show steps
-
Review the course materials and identify key concepts.
-
Attempt the practice problems provided in the course modules.
-
Check your answers against the provided solutions.
-
Seek clarification from the instructors or peers if needed.
Write a blog post on an NLP topic
Show steps
Writing a blog post will help you synthesize and communicate your understanding of NLP concepts and techniques.
Browse courses on
NLP
Show steps
-
Choose a specific NLP topic to write about, such as a particular model, technique, or application.
-
Research the topic thoroughly and gather relevant information.
-
Write a clear and engaging blog post that explains the topic in a way that is accessible to a general audience.
Explore Hands-On Tutorials for Transformers
Show steps
Deepen your practical knowledge by following guided tutorials that demonstrate the implementation and application of Transformers in real-world scenarios.
Browse courses on
Transformers
Show steps
-
Identify reputable sources for Transformers tutorials.
-
Select a tutorial that aligns with your interests and skill level.
-
Follow the tutorial step-by-step, implementing the code and experimenting with different parameters.
-
Troubleshoot any issues you encounter and seek support from the tutorial community or instructors.
-
Reflect on your learning and identify areas for further exploration.
Develop a chatbot prototype
Show steps
Apply knowledge by building a practical application, showcasing proficiency in designing and implementing language-based conversational systems.
Show steps
-
Design the chatbot's conversational flow and user interface.
-
Train the chatbot using a suitable dataset and fine-tuning techniques.
-
Deploy the chatbot on a platform and gather feedback for further improvement.
Develop a simple text classification model
Show steps
Showcase your mastery of language modeling through the creation of a text classification model.
Browse courses on
Text Classification
Show steps
-
Gather a dataset of labeled text data.
-
Select an appropriate machine learning algorithm and metrics.
-
Train and evaluate the model using established techniques.
-
Refine and optimize the model's performance.
Follow tutorials on advanced NLP topics
Show steps
Exploring advanced NLP topics through tutorials will expand your knowledge and understanding of the field.
Browse courses on
Transformers
Show steps
-
Follow tutorials on transformer models, such as BERT or GPT-3.
-
Learn about techniques for fine-tuning large language models for specific tasks.
-
Investigate the ethical implications and challenges of using large language models.
Attend a workshop on advanced NLP techniques
Show steps
Enrich your knowledge and connect with experts by participating in an advanced NLP workshop.
Browse courses on
NLP
Show steps
-
Research and identify a relevant NLP workshop.
-
Register for and attend the workshop.
-
Engage actively in discussions and hands-on exercises.
-
Network with professionals in the NLP field.
Build a Mini Language Model
Show steps
Apply your knowledge by creating a small-scale language model, allowing you to gain hands-on experience with model development, training, and evaluation.
Browse courses on
Language Modeling
Show steps
-
Choose a suitable dataset and prepare it for modeling.
-
Select or design a language model architecture.
-
Train your model on the prepared dataset.
-
Evaluate the performance of your model using appropriate metrics.
-
Refine your model based on the evaluation results and consider deploying it for a specific application.
Review Python basics
Show steps
Solidify your understanding of the syntax and fundamentals of Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials
- Refresh your memory on the official Python documentation.
- Solve basic coding problems using online platforms.
Review natural language processing fundamentals
Show steps
Strengthen the foundation by revisiting core concepts in natural language processing, as they are essential for understanding large language models.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Go through lecture notes or textbooks on topics like tokenization, stemming, and part-of-speech tagging.
- Practice solving problems related to text preprocessing and feature extraction.
Recall the concept of natural language processing
Show steps
Enhance your understanding of the fundamental principles of natural language processing techniques.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Revise your notes from previous courses or online resources.
- Explore introductory materials and tutorials on NLP.
- Participate in online forums or discussion groups on NLP.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Create a knowledge base
Show steps
Improve retention and organization by compiling notes, assignments, and other course materials into a comprehensive knowledge base.
Show steps
Show steps
Show steps
- Gather and organize lecture notes, slides, and handouts.
- Summarize key concepts and important ideas.
- Add additional resources, such as relevant articles and videos, to complement the materials.
Review basics of NLP
Show steps
Reviewing basic concepts in NLP will help you acclimate to the technical terminology and ideas used in this course.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Review foundational concepts of NLP, including tokenization, stemming, and part-of-speech tagging.
- Review different types of NLP tasks, such as text classification, named entity recognition, and machine translation.
- Identify and understand key algorithms and techniques used in NLP, such as TF-IDF, word embeddings, and neural networks.
Join Peer Discussion Groups
Show steps
Engage with fellow learners by participating in discussion groups, exchanging insights, and providing support, which can enhance your understanding of the course topics.
Browse courses on
Discussion
Show steps
Show steps
Show steps
- Identify active discussion forums or create your own group.
- Participate in discussions by sharing your perspectives, asking questions, and responding to others.
- Actively listen to diverse viewpoints and engage in respectful discussions.
- Collaborate on projects or assignments to gain different perspectives and improve your teamwork skills.
Engage in discussion forums
Show steps
Promote active learning by participating in discussions with peers, exchanging insights, and clarifying concepts related to large language models.
Show steps
Show steps
Show steps
- Participate in online forums dedicated to natural language processing and machine learning.
- Ask questions, share ideas, and engage with other learners to enhance understanding.
Practice coding exercises
Show steps
Improve understanding of foundational concepts through hands-on exercises related to language models and their programming framework.
Show steps
Show steps
Show steps
- Solve hands-on coding problems on platforms like LeetCode or HackerRank
- Implement various types of language models, such as LSTM, GRU, and Transformer, in Python.
- Experiment with different hyperparameters and training datasets to optimize model performance.
Practice Python coding for NLP tasks
Show steps
Practicing Python coding will help you develop the skills necessary to implement and experiment with NLP algorithms.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Solve coding exercises on Python libraries for NLP, such as NLTK or spaCy.
- Implement NLP algorithms from scratch, such as a bag-of-words model or a simple neural network for text classification.
Explore advanced Transformer architectures
Show steps
Enhance understanding of the theoretical foundations and architectural advancements in Transformer models.
Show steps
Show steps
Show steps
- Follow research papers and blog posts on topics such as self-attention, encoder-decoder architectures, and pre-training techniques.
- Attend webinars or workshops conducted by experts in the field.
Practice text generation with pre-trained models
Show steps
Develop your practical skills in using pre-trained models for text-based tasks.
Browse courses on
Text Generation
Show steps
Show steps
Show steps
- Identify a pre-trained language model such as GPT-3 or similar.
- Access the model's API or interface.
- Experiment with different prompts and parameters to generate text.
- Analyze the generated text for quality and relevance.
Build a simple text classification model
Show steps
Building a text classification model will allow you to apply the concepts and techniques learned in the course to a practical problem.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Choose a dataset for text classification.
- Preprocess the data, including tokenization, stemming, and creating feature vectors.
- Train a machine learning model for text classification, such as logistic regression or a neural network.
- Evaluate the performance of the model using metrics such as accuracy and F1 score.
Complete Practice Problems
Show steps
Sharpen your understanding of the fundamental concepts and techniques discussed in the course by working through practice problems.
Browse courses on
Language Modeling
Show steps
Show steps
Show steps
- Review the course materials and identify key concepts.
- Attempt the practice problems provided in the course modules.
- Check your answers against the provided solutions.
- Seek clarification from the instructors or peers if needed.
Write a blog post on an NLP topic
Show steps
Writing a blog post will help you synthesize and communicate your understanding of NLP concepts and techniques.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Choose a specific NLP topic to write about, such as a particular model, technique, or application.
- Research the topic thoroughly and gather relevant information.
- Write a clear and engaging blog post that explains the topic in a way that is accessible to a general audience.
Explore Hands-On Tutorials for Transformers
Show steps
Deepen your practical knowledge by following guided tutorials that demonstrate the implementation and application of Transformers in real-world scenarios.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Identify reputable sources for Transformers tutorials.
- Select a tutorial that aligns with your interests and skill level.
- Follow the tutorial step-by-step, implementing the code and experimenting with different parameters.
- Troubleshoot any issues you encounter and seek support from the tutorial community or instructors.
- Reflect on your learning and identify areas for further exploration.
Develop a chatbot prototype
Show steps
Apply knowledge by building a practical application, showcasing proficiency in designing and implementing language-based conversational systems.
Show steps
Show steps
Show steps
- Design the chatbot's conversational flow and user interface.
- Train the chatbot using a suitable dataset and fine-tuning techniques.
- Deploy the chatbot on a platform and gather feedback for further improvement.
Develop a simple text classification model
Show steps
Showcase your mastery of language modeling through the creation of a text classification model.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Gather a dataset of labeled text data.
- Select an appropriate machine learning algorithm and metrics.
- Train and evaluate the model using established techniques.
- Refine and optimize the model's performance.
Follow tutorials on advanced NLP topics
Show steps
Exploring advanced NLP topics through tutorials will expand your knowledge and understanding of the field.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Follow tutorials on transformer models, such as BERT or GPT-3.
- Learn about techniques for fine-tuning large language models for specific tasks.
- Investigate the ethical implications and challenges of using large language models.
Attend a workshop on advanced NLP techniques
Show steps
Enrich your knowledge and connect with experts by participating in an advanced NLP workshop.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Research and identify a relevant NLP workshop.
- Register for and attend the workshop.
- Engage actively in discussions and hands-on exercises.
- Network with professionals in the NLP field.
Build a Mini Language Model
Show steps
Apply your knowledge by creating a small-scale language model, allowing you to gain hands-on experience with model development, training, and evaluation.
Browse courses on
Language Modeling
Show steps
Show steps
Show steps
- Choose a suitable dataset and prepare it for modeling.
- Select or design a language model architecture.
- Train your model on the prepared dataset.
- Evaluate the performance of your model using appropriate metrics.
- Refine your model based on the evaluation results and consider deploying it for a specific application.
Career center
Learners who complete Generative Pre-trained Transformers (GPT) will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Developer
Artificial Intelligence Developer
Artificial Intelligence Developers explore and develop artificial intelligence (AI) solutions to meet various needs. They innovate and improve AI products and services. Those in this role may find this course helpful as it provides a good understanding of the fundamental concepts of natural language processing and language modeling, which would be beneficial when working with language-based AI applications.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They need to have a strong understanding of market research, advertising, and public relations. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate marketing materials that are effective and engaging.
Data Scientist
Data Scientist
Data Scientists use their expertise in mathematics, statistics, and computer science to extricate insights from complex datasets. They must be able to communicate these insights to stakeholders in a clear and concise manner. This course may be helpful for those in this role, as it can improve understanding of the building blocks of Transformers and teaches how to apply them for generating new text.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for creating, deploying, and maintaining machine learning (ML) models. They must be able to translate business problems into ML solutions. This course may be helpful because it explores the technical background for neural language models and provides an overview of how they are used to generate text, which could be useful for any ML-based role.
Business Analyst
Business Analyst
Business Analysts help organizations understand their business needs and develop solutions to meet those needs. They need to have a strong understanding of business processes and data analysis. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate reports and presentations that are clear and concise.
Content Writer
Content Writer
Content Writers create written content for various platforms, including websites, blogs, social media, and more. They need to have a strong understanding of grammar, style, and audience. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate engaging content.
Computational Linguist
Computational Linguist
Computational Linguists apply their knowledge of computer science and linguistics to solve problems related to natural language. This course may be helpful for those in this role as it explores the basics of how language models work, and the specifics of how newer neural-based approaches are built.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. They need to have a strong understanding of business strategy, operations, and finance. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate reports and presentations that are clear and concise.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They need to have a strong understanding of market research, product development, and marketing. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate marketing materials that are effective and engaging.
Technical Writer
Technical Writer
Technical Writers create documentation and other written materials that explain complex technical information. They need to have a strong understanding of both technical concepts and writing principles. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate clear and concise technical documentation.
Public relations manager
Public relations manager
Public Relations Managers are responsible for managing the public image of organizations. They need to have a strong understanding of media relations, crisis communication, and social media. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate press releases and other public relations materials that are clear and concise.
User Experience Designer
User Experience Designer
User Experience Designers create products and services that are easy to use and enjoyable. They need to have a strong understanding of user behavior and interface design. This course may be helpful for those in this role as it can help them understand the nuances of language and how to generate interfaces that are intuitive and user-friendly.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with various programming languages and technologies to create robust and reliable software. This course may be helpful for those in this role as it teaches the basics of how language models work, and the specifics of how newer neural-based approaches are built. This could help them create software that uses natural language processing or language modeling.
Web Developer
Web Developer
Web Developers design, create, and maintain websites. They need to have a strong understanding of programming languages, web technologies, and user interface design. This course may be helpful for those in this role as it provides a good foundation in natural language processing. This knowledge can be used to create more dynamic and interactive websites.
Research Scientist
Research Scientist
Research Scientists are involved in the conception, design, execution, and analysis of scientific research projects. They need to be able to analyze data, draw conclusions, and communicate their findings. This course could be useful for anyone in this role as it introduces the fundamental ideas of natural language processing and language modeling. This could be beneficial for many kinds of research, including some in the field of computer science.
For more career information including salaries, visit:
OpenCourser.com/course/8fy8by/generative
Reading list
We've selected ten 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
Generative Pre-trained Transformers (GPT).
Provides a comprehensive overview of pattern recognition and machine learning, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of statistical learning.
Provides a comprehensive overview of deep learning for NLP, including the basic concepts, model architectures, and applications. It valuable resource for readers who want to gain a strong foundation in the field of NLP.
Provides a comprehensive overview of statistical NLP, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of statistical NLP.
Provides a comprehensive overview of machine learning for text, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of machine learning for text.
Provides a comprehensive overview of speech and language processing, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of speech and language processing.
Save
Provides a comprehensive overview of deep learning, including the basic concepts, models, and applications. It valuable resource for readers who want to gain a strong foundation in the field of deep learning.
Provides a practical introduction to machine learning for hackers, including the basic concepts, techniques, and applications. It valuable resource for readers who want to get started with machine learning or who want to learn more about its practical applications.
Provides a practical introduction to NLP, including the basic concepts, techniques, and applications. It valuable resource for readers who want to get started with NLP or who want to learn more about its practical applications.
Provides a practical introduction to NLP with Python, including the basic concepts, techniques, and applications. It valuable resource for readers who want to get started with NLP or who want to learn more about its practical applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8fy8by/generative
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Effort
3 weeks of study, 3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Glasgow
Instructors
Mary Ellen Foster
Sean MacAvaney
Jake Lever
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
Good to know
Explores deep learning models, particularly GPT-3.5, which are rapidly shaping numerous industries
Introduces the basics of language modelling and its relevance in understanding and generating text
Taught by seasoned experts, including Mary Ellen Foster, Sean MacAvaney, and Jake Lever
Provides hands-on labs and interactive materials for practical application of concepts
Suitable for individuals seeking a strong foundation in natural language processing and language modelling
Covers the key innovations that have enabled Transformer-based large language models to excel
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