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Charles Ivan Niswander II
In this 1-hour long project-based course, we will explore Transformer-based Natural Language Processing. Specifically, we will be taking a look at re-training or fine-tuning GPT-2, which is an NLP machine learning model based on the Transformer architecture....
Read more
In this 1-hour long project-based course, we will explore Transformer-based Natural Language Processing. Specifically, we will be taking a look at re-training or fine-tuning GPT-2, which is an NLP machine learning model based on the Transformer architecture....
Read more
In this 1-hour long project-based course, we will explore Transformer-based Natural Language Processing. Specifically, we will be taking a look at re-training or fine-tuning GPT-2, which is an NLP machine learning model based on the Transformer architecture. We will cover the history of GPT-2 and it's development, cover basics about the Transformer architecture, learn what type of training data to use and how to collect it, and finally, perform the fine tuning process. In the final task, we will discuss use cases and what the future holds for Transformer-based NLP.
I would encourage learners to do further research and experimentation with the GPT-2 model, as well as other NLP models!
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/pkpfy9/deep
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what to watch for , and
possible dealbreakers
Develops GPT-2, an NLP ML model, for use in industry, personal growth, and research
Builds a strong foundation in NLP machine learning and the Transformer architecture
Led by Charles Ivan Niswander II, who is recognized for their work in the industry
Emphasizes hands-on training through fine-tuning, fostering practical skills
Teaches skills and knowledge that are highly relevant to industry practices
May require additional materials and goods beyond what is readily available
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Reviews summary
Short, practical deep learning
This course delves into Transformer-based Natural Language Processing, particularly the retraining and fine-tuning of the GPT-2 model. With eight 5-star and five 2-star reviews, this course receives mixed reviews. Some students find it well-structured, easy to follow, and enjoyable, but others critique the lack of interactivity, missing resources, and technical issues.
Explanations are clear and simple, making the learning process more accessible.
"I enjoyed the simplicity of how things were explained."
The course provides practical, hands-on experience with the GPT-2 model.
Virtual environment issues and missing files can hinder the learning experience.
"sometime the window would expand and collapse on its own..."
"Virtual environment is very glitchy..."
Some resources, like the scrape file, were missing, causing inconvenience.
"The scrape file was missing."
"...missing file in examples..."
Insufficient guidance and lack of clarity about the project workflow can cause confusion.
"there was no reference or guide given on how to attend the guided project..."
"Also, one of the files mentioned were missing."
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 Deep Learning NLP: Training GPT-2 from scratch with these
activities:
Compile and organize your course notes, assignments, and practice exercises
Show steps
Enhance your understanding and retention of course materials by organizing and reviewing them regularly.
Show steps
-
Gather all your course notes, assignments, and practice exercises
-
Organize the materials into a structured format
-
Review the materials regularly to reinforce your learning
Review the OpenAI blog
Show steps
Gain background information and context about the GPT-2 model and its development.
Browse courses on
OpenAI
Show steps
-
Visit the OpenAI blog website
-
Read articles related to GPT-2 and its development
-
Take notes on key concepts and ideas
Explore the Hugging Face Transformers library
Show steps
Develop practical skills in using the Hugging Face Transformers library for NLP tasks.
Browse courses on
Hugging Face Transformers
Show steps
-
Visit the Hugging Face Transformers documentation
-
Follow tutorials on using the library for GPT-2 fine-tuning
-
Experiment with different parameters and settings
Show all three activities
Compile and organize your course notes, assignments, and practice exercises
Show steps
Enhance your understanding and retention of course materials by organizing and reviewing them regularly.
Show steps
Show steps
Show steps
- Gather all your course notes, assignments, and practice exercises
- Organize the materials into a structured format
- Review the materials regularly to reinforce your learning
Review the OpenAI blog
Show steps
Gain background information and context about the GPT-2 model and its development.
Browse courses on
OpenAI
Show steps
Show steps
Show steps
- Visit the OpenAI blog website
- Read articles related to GPT-2 and its development
- Take notes on key concepts and ideas
Explore the Hugging Face Transformers library
Show steps
Develop practical skills in using the Hugging Face Transformers library for NLP tasks.
Browse courses on
Hugging Face Transformers
Show steps
Show steps
Show steps
- Visit the Hugging Face Transformers documentation
- Follow tutorials on using the library for GPT-2 fine-tuning
- Experiment with different parameters and settings
Show all three activities
Career center
Learners who complete Deep Learning NLP: Training GPT-2 from scratch will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Scientist
Natural Language Processing Scientist
Natural Language Processing Scientists lead teams on cutting edge research and development of language-based artificial intelligences. This course will introduce you to some of the NLP principles that these scientists must have an understanding of, such as a Transformer-based neural network.
Natural Language Research Scientist
Natural Language Research Scientist
A Natural Language Research Scientist develops new techniques and algorithms for Natural Language Processing models. This course will introduce some foundational principles found in the work of many in this field, such as transformer neural networks and fine-tuning.
Text Engineer
Text Engineer
Text Engineers work on collecting and preparing text data for use by machine learning models. This course will introduce some of the NLP concepts that are essential to this role.
Conversational AI Developer
Conversational AI Developer
Conversational AI Developers work on developing and maintaining conversational agents that interact with humans using natural language. This course will introduce foundational concepts like transformer neural networks that are essential to this work.
Computational Linguist
Computational Linguist
Computational Linguists use both natural language processing and linguistics to study language. They apply linguistic theory to computational problems. This course will give you experience with some of the foundational concepts used by these professionals, especially in relation to transformer neural networks.
Data Scientist
Data Scientist
Data Scientists have a strong foundation in machine learning and statistics. This course will also introduce you to some of the foundational concepts of machine learning and how it is used to train powerful artificial intelligences.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the machine learning models that power many of today's most cutting edge technologies. This course will give you hands on experience at the foundational concepts used in those environments, such as learning how to fine-tune large language models.
Software Developer
Software Developer
Software Developers work on building and maintaining complex computer programs. This course will introduce you to core machine learning concepts that are used in many applications such as anomaly detection and automating functions based on data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use complex machine learning models to aid financial decision making. This course will give you experience working with some of the core concepts used in the field, such as natural language processing.
Data Analyst
Data Analyst
Data Analysts collect, clean, and explore data to help make data-driven decisiones. This course will introduce you to some of the foundational concepts used by many in this role, such as working with text data and training machine learning models.
Business Analyst
Business Analyst
Business Analysts translate business requirements into technical requirements and vice versa. They bridge the gap between the business and technical teams. This course will give you foundational experience using some of the tools used by business analysts, such as machine learning.
Product Manager
Product Manager
Product Managers lead the development of new products. They work with engineers, designers, and marketers to bring new products to market. This course will introduce you to foundational concepts such as machine learning and natural language processing, which are commonly used in modern products.
Technical Writer
Technical Writer
Technical Writers write documentation for software and other technical products. This course will introduce you to foundational concepts such as machine learning and artificial intelligence, which are used in many of today's technical products.
Project Manager
Project Manager
Project Managers oversee the development of new products or services. They work with a variety of stakeholders to ensure that projects are completed on time and within budget. This course will give you experience using some of the foundational concepts used by product managers, such as machine learning.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and use technical products. They work with customers to identify their needs and recommend solutions. This course will give you experience using some of the foundational concepts used by sales engineers, such as machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/pkpfy9/deep
Reading list
We've selected 13 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
Deep Learning NLP: Training GPT-2 from scratch.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular open-source library for natural language processing. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Save
Provides a comprehensive overview of text mining techniques with R. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Save
Provides a comprehensive overview of natural language processing with TensorFlow. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Save
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Save
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including word embeddings, convolutional neural networks, and recurrent neural networks.
Save
Provides a comprehensive overview of machine learning techniques for text. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
A practical guide to deep learning using Python, covering the fundamentals of neural networks and their applications. Provides a solid foundation for understanding the implementation details of GPT-2 and other deep learning models.
Provides a comprehensive overview of the statistical foundations of natural language processing. It covers a wide range of topics, including probability theory, information theory, and machine learning.
A hands-on guide to NLP using Python, covering both traditional and deep learning approaches. Provides practical examples and exercises to reinforce the concepts discussed in the course.
Provides a comprehensive overview of natural language processing, including chapters on machine learning and neural networks. Offers a broad context for understanding the role of Transformers in NLP.
An accessible and comprehensive overview of deep learning, covering both theoretical concepts and practical applications. Provides a gentle introduction to the field, suitable for beginners with limited prior knowledge.
A beginner-friendly introduction to machine learning, covering both theoretical concepts and practical applications. Provides a gentle introduction to the field, suitable for learners with no prior knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pkpfy9/deep
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Develops GPT-2, an NLP ML model, for use in industry, personal growth, and research
Builds a strong foundation in NLP machine learning and the Transformer architecture
Led by Charles Ivan Niswander II, who is recognized for their work in the industry
Emphasizes hands-on training through fine-tuning, fostering practical skills
Teaches skills and knowledge that are highly relevant to industry practices
May require additional materials and goods beyond what is readily available
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