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Snehan Kekre
This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub.
Read more
This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub.
Read more
This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub.
Prerequisites:
In order to successfully complete this project, you should be competent in the Python programming language, be familiar with deep learning for Natural Language Processing (NLP), and have trained models with TensorFlow or and its Keras API.
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/7o08dd/fine
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Syllabus
Fine Tune BERT for Text Classification with TensorFlow
This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses heavily on teaching students how to fine-tune BERT models, which is important for students looking to enter the field of Natural Language Processing (NLP)
Emphasizes practical implementation, guiding learners through the process of working with real-world data and utilizing TensorFlow and TensorFlow Hub to train and evaluate BERT models
Well-suited for learners with prior experience in Python, deep learning for NLP, and model training with TensorFlow or Keras API, catering to individuals with a strong technical foundation
Primarily targets learners based in the North America region, which may limit accessibility for individuals in other geographical locations
Save this course
Save Fine Tune BERT for Text Classification with TensorFlow to your list so you can find it easily later:
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Reviews summary
Bert fine-tuning mastery
Learners say this course is a well put together project focused on fine-tuning Bidirectional Encoder Representations from Transformers (BERT) with TensorFlow to classify text. Despite some reviewers wanting more detail on the theoretical underpinnings for BERT and the Transformer architecture, learners largely agree that this project is incredibly helpful and well-paced. It's noted by many that this course is especially good for those who already have some prior knowledge of BERT, TensorFlow, and NLP concepts such as Transformers and attention.
While some learners want more introductory content, the majority find that this course is a useful next step for those who have prior knowledge.
"Need More detail explanation as its a advance NLP topic. "
"Excellent course for those who already has done some research on the field."
"Helped me cement the basic understanding on how to use BERT for my use case."
The instructor is praised for clear explanations and the ability to teach concepts in a straightforward way.
"Excellent and very helpful course, the instructor language is very clear and concise and to the point, I would love to learn more from the same instructor."
"This is such a great course !!!! The instructor prepared the knowledge very well, and he is so good at teaching !"
"colab was extremly slow but the teacher was great"
Learners consistently emphasize the usefulness of having existing knowledge on BERT and TensorFlow.
"Background in BERT and TemsorFlow needed. Some things where difficult to follow"
"Great Intro to BERT! Would recommend needing to have good skills with Python, Tensorflow and some knowledge of BERT and concepts of NLP like Transformers, Attention, etc to take full advantage of the same! :D"
"It's good to learn how to implement BERT model with pyTorch.Personally, I need more theoretical instructions about BERT and transformer."
The course project is consistently praised as helpful, well-structured, and easy to follow.
"The project is well designed, helpful. I learn a lot from this project. Thank you very much"
"The project was well explained and provided good understanding of bert for text classification. Also the quiz were good. "
"The project is very clear and easy to follow."
A few learners mention technical issues, especially with Google Colab running slowly.
"Unable to complete the course because the version of tensorflow used in it doesn't play nice anymore with the collab environment the course use :("
"colab was extremly slow but the teacher was great"
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 Fine Tune BERT for Text Classification with TensorFlow with these
activities:
TensorFlow Refresher
Show steps
Refresh TensorFlow skills to support effective learning in this course
Browse courses on
TensorFlow
Show steps
-
Review TensorFlow data manipulation functions
-
Practice building and training neural network models in TensorFlow
Review the basics of machine learning and deep learning
Show steps
Strengthens foundation in machine learning and deep learning concepts, enhancing understanding of BERT and text classification.
Show steps
-
Revisit foundational concepts in machine learning, such as supervised learning, feature engineering, and model evaluation.
-
Review key principles of deep learning, including neural networks, activation functions, and optimization algorithms.
TensorFlow Hub and tf.data API Review
Show steps
Refresh understanding of key concepts and techniques for smooth learning
Browse courses on
TensorFlow Hub
Show steps
-
Review TensorFlow Hub's pre-trained models and their usage
-
Study tf.data API for building efficient data input pipelines
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
BERT Fundamentals Review
Show steps
Ensure a foundational understanding of BERT and preparatory techniques for enhanced learning
Browse courses on
BERT
Show steps
-
Review basic principles of Transformers and BERT
-
Study text tokenization and preprocessing methods
Compile a collection of resources on BERT for text classification
Show steps
Facilitates future reference and continued learning on BERT and text classification.
Show steps
-
Gather relevant articles, tutorials, and documentation on BERT and text classification.
-
Organize the resources into a logical structure, such as categories or topics.
-
Add brief annotations or summaries to each resource for easy reference.
Deep Learning with Python, 2nd Edition
Show steps
Expand understanding of deep learning, particularly with TensorFlow, for better comprehension of BERT
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read chapters on Tensorflow basics and fundamentals
-
Study deep learning architectures and techniques
BERT API Overview Tutorial
Show steps
Deepen understanding of BERT classification concepts by working through a tutorial on the official TensorFlow Hub BERT API.
Show steps
-
Read the provided TensorFlow Hub BERT API documentation
-
Follow the tutorial steps to build a basic text classification model
-
Experiment with different parameters and data inputs
Follow a TensorFlow tutorial on text classification with BERT
Show steps
Solidifies understanding of BERT text classification concepts and provides hands-on practice with TensorFlow.
Show steps
-
Locate a comprehensive TensorFlow tutorial on text classification using BERT.
-
Follow the tutorial step-by-step, implementing the code and experimenting with different parameters.
-
Review the results and compare them to the provided examples.
Complete practice exercises on fine-tuning BERT models
Show steps
Strengthens understanding of fine-tuning parameters and improves accuracy in implementing BERT models.
Show steps
-
Find a set of practice exercises or problems related to fine-tuning BERT models.
-
Attempt to solve the exercises independently, referring to course materials for guidance.
-
Compare solutions with provided answers or consult online forums for feedback.
Assist fellow students in understanding BERT and text classification
Show steps
Reinforces understanding through teaching and clarifies concepts for both the mentor and mentee.
Show steps
-
Identify opportunities to assist classmates or learners in online forums or study groups.
-
Provide clear explanations, share resources, and engage in discussions related to BERT and text classification.
Develop a text classification application using a fine-tuned BERT model
Show steps
Provides practical experience in applying BERT for real-world text classification tasks.
Show steps
-
Identify a specific text classification problem to address.
-
Gather and prepare a suitable dataset for the task.
-
Fine-tune a BERT model on the dataset using the techniques learned in the course.
-
Develop a user interface for the application and integrate the fine-tuned model.
-
Test and evaluate the application's performance.
Participate in a Kaggle competition on text classification
Show steps
Provides a challenging and practical environment to apply BERT text classification skills and compete with others.
Show steps
-
Identify a relevant Kaggle competition focused on text classification.
-
Obtain the competition dataset and familiarize yourself with the task.
-
Fine-tune a BERT model for the competition, experiment with different parameters, and track your progress.
-
Submit your model and compare your results with other participants.
TensorFlow Refresher
Show steps
Refresh TensorFlow skills to support effective learning in this course
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review TensorFlow data manipulation functions
- Practice building and training neural network models in TensorFlow
Review the basics of machine learning and deep learning
Show steps
Strengthens foundation in machine learning and deep learning concepts, enhancing understanding of BERT and text classification.
Show steps
Show steps
Show steps
- Revisit foundational concepts in machine learning, such as supervised learning, feature engineering, and model evaluation.
- Review key principles of deep learning, including neural networks, activation functions, and optimization algorithms.
TensorFlow Hub and tf.data API Review
Show steps
Refresh understanding of key concepts and techniques for smooth learning
Browse courses on
TensorFlow Hub
Show steps
Show steps
Show steps
- Review TensorFlow Hub's pre-trained models and their usage
- Study tf.data API for building efficient data input pipelines
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
BERT Fundamentals Review
Show steps
Ensure a foundational understanding of BERT and preparatory techniques for enhanced learning
Browse courses on
BERT
Show steps
Show steps
Show steps
- Review basic principles of Transformers and BERT
- Study text tokenization and preprocessing methods
Compile a collection of resources on BERT for text classification
Show steps
Facilitates future reference and continued learning on BERT and text classification.
Show steps
Show steps
Show steps
- Gather relevant articles, tutorials, and documentation on BERT and text classification.
- Organize the resources into a logical structure, such as categories or topics.
- Add brief annotations or summaries to each resource for easy reference.
Deep Learning with Python, 2nd Edition
Show steps
Expand understanding of deep learning, particularly with TensorFlow, for better comprehension of BERT
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read chapters on Tensorflow basics and fundamentals
- Study deep learning architectures and techniques
BERT API Overview Tutorial
Show steps
Deepen understanding of BERT classification concepts by working through a tutorial on the official TensorFlow Hub BERT API.
Show steps
Show steps
Show steps
- Read the provided TensorFlow Hub BERT API documentation
- Follow the tutorial steps to build a basic text classification model
- Experiment with different parameters and data inputs
Follow a TensorFlow tutorial on text classification with BERT
Show steps
Solidifies understanding of BERT text classification concepts and provides hands-on practice with TensorFlow.
Show steps
Show steps
Show steps
- Locate a comprehensive TensorFlow tutorial on text classification using BERT.
- Follow the tutorial step-by-step, implementing the code and experimenting with different parameters.
- Review the results and compare them to the provided examples.
Complete practice exercises on fine-tuning BERT models
Show steps
Strengthens understanding of fine-tuning parameters and improves accuracy in implementing BERT models.
Show steps
Show steps
Show steps
- Find a set of practice exercises or problems related to fine-tuning BERT models.
- Attempt to solve the exercises independently, referring to course materials for guidance.
- Compare solutions with provided answers or consult online forums for feedback.
Assist fellow students in understanding BERT and text classification
Show steps
Reinforces understanding through teaching and clarifies concepts for both the mentor and mentee.
Show steps
Show steps
Show steps
- Identify opportunities to assist classmates or learners in online forums or study groups.
- Provide clear explanations, share resources, and engage in discussions related to BERT and text classification.
Develop a text classification application using a fine-tuned BERT model
Show steps
Provides practical experience in applying BERT for real-world text classification tasks.
Show steps
Show steps
Show steps
- Identify a specific text classification problem to address.
- Gather and prepare a suitable dataset for the task.
- Fine-tune a BERT model on the dataset using the techniques learned in the course.
- Develop a user interface for the application and integrate the fine-tuned model.
- Test and evaluate the application's performance.
Participate in a Kaggle competition on text classification
Show steps
Provides a challenging and practical environment to apply BERT text classification skills and compete with others.
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition focused on text classification.
- Obtain the competition dataset and familiarize yourself with the task.
- Fine-tune a BERT model for the competition, experiment with different parameters, and track your progress.
- Submit your model and compare your results with other participants.
Career center
Learners who complete Fine Tune BERT for Text Classification with TensorFlow will develop knowledge and skills
that may be useful to these careers:
NLP Software Engineer
NLP Software Engineer
NLP Software Engineers develop and enhance natural language processing software applications, which are used to analyze and understand human language. This course can help lead to success in the NLP Software Engineer role by providing a foundation in the use of Bidirectional Transformers for Language Understanding (BERT) models for text classification, a fundamental task in NLP. By learning to fine-tune a BERT model with TensorFlow, you will gain the technical skills necessary to build and deploy NLP systems that can effectively handle text data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models for various applications. This course can help you succeed in this role by providing hands-on experience fine-tuning a BERT model for text classification, a task commonly encountered in machine learning projects. You will learn how to preprocess and tokenize data, build TensorFlow input pipelines, and train and evaluate a fine-tuned BERT model. These skills are essential for building effective machine learning models for text-based tasks.
Data Scientist
Data Scientist
Data Scientists analyze data to extract insights and provide recommendations for decision-making. This course can be helpful for aspiring Data Scientists by providing a practical understanding of how to use BERT models for text classification, a common task in data science projects. By learning to fine-tune a BERT model with TensorFlow, you will gain the skills to handle text data effectively and derive meaningful insights from it.
Natural Language Processing Researcher
Natural Language Processing Researcher
Natural Language Processing Researchers explore and develop new methods for computers to understand and generate human language. This course can support your research by providing hands-on experience fine-tuning a BERT model for text classification, a fundamental task in NLP. You will gain insights into the inner workings of BERT models and learn how to adapt them for specific text classification tasks. This knowledge can contribute to your research and help you develop novel NLP solutions.
Computational Linguist
Computational Linguist
Computational Linguists study the intersection of language and computation, developing computational models for language analysis and generation. This course can enhance your skills as a Computational Linguist by providing practical experience fine-tuning a BERT model for text classification. You will learn how to apply deep learning techniques to NLP tasks, gaining a deeper understanding of how computers process and understand language.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and build AI systems, applying machine learning and deep learning techniques to solve real-world problems. This course can be beneficial for AI Engineers by providing hands-on experience fine-tuning a BERT model for text classification, a common task in AI applications. You will learn how to leverage TensorFlow and BERT to develop AI models that can effectively handle and analyze text data.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to support decision-making. This course can enhance your skills as a Data Analyst by providing practical experience fine-tuning a BERT model for text classification, a common task in data analysis. You will learn how to use BERT models to extract meaningful insights from text data, enabling you to make more informed decisions.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course can be helpful for Software Developers who want to specialize in NLP or AI by providing hands-on experience fine-tuning a BERT model for text classification. You will learn how to build and deploy NLP models using TensorFlow, a valuable skill for developing software applications that can process and understand text data effectively.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, ensuring it is clear, accurate, and accessible. This course can be helpful for Technical Writers by providing hands-on experience fine-tuning a BERT model for text classification, a skill that can enhance your ability to analyze and organize technical information effectively. You will learn how to apply NLP techniques to improve the structure and readability of technical documentation.
Product Manager
Product Manager
Product Managers are responsible for the development and success of products, ensuring they meet customer needs and business objectives. This course can be useful for aspiring Product Managers by providing insights into the use of BERT models for text classification, a task often encountered in product development. You will gain an understanding of how NLP can be applied to improve user experience, product recommendations, and customer feedback analysis.
Content Strategist
Content Strategist
Content Strategists plan, create, and manage content for various platforms and audiences. This course can be useful for Content Strategists by providing practical experience fine-tuning a BERT model for text classification, a valuable skill for content optimization and analysis. You will learn how to use BERT models to categorize and analyze content, enabling you to develop more effective and engaging content strategies.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing campaigns and data to optimize performance and measure ROI. This course can be useful for Marketing Analysts by providing hands-on experience fine-tuning a BERT model for text classification, a skill that can enhance your ability to analyze customer feedback, social media data, and marketing content. You will learn how to use BERT models to extract insights from text data, enabling you to make more informed marketing decisions.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve efficiency and productivity. This course can be useful for Business Analysts by providing practical experience fine-tuning a BERT model for text classification, a skill that can enhance your ability to analyze business documents, customer feedback, and market research data. You will learn how to use BERT models to extract insights from text data, enabling you to make more informed business decisions.
Information Architect
Information Architect
Information Architects design and organize information systems to make them accessible and usable. This course can be useful for Information Architects who want to specialize in NLP or AI by providing hands-on experience fine-tuning a BERT model for text classification. You will learn how to build and deploy NLP models using TensorFlow, a valuable skill for developing information systems that can effectively handle and analyze text data.
Project Manager
Project Manager
Project Managers plan, organize, and manage projects to ensure their successful completion. This course may be useful for Project Managers who want to gain a basic understanding of NLP and AI by providing an introduction to fine-tuning a BERT model for text classification. You will learn the fundamentals of BERT models and how they can be applied to real-world projects, enabling you to make more informed decisions and lead projects that involve NLP and AI.
For more career information including salaries, visit:
OpenCourser.com/course/7o08dd/fine
Reading list
We've selected eight 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
Fine Tune BERT for Text Classification with TensorFlow.
Provides a collection of recipes for solving common problems in TensorFlow 2.0. It covers a wide range of topics, including data preprocessing, model training, and deployment. This book would be a valuable resource for anyone interested in learning more about TensorFlow 2.0.
Provides a comprehensive introduction to machine learning with TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning. This book would be a valuable resource for anyone interested in learning more about machine learning with TensorFlow.
Provides a practical guide to deep learning with Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks. This book would be a valuable resource for anyone interested in learning more about deep learning with Python.
Provides a comprehensive overview of natural language processing with Python. It covers the different types of NLP tasks, as well as the different Python libraries that can be used for NLP. This book would be a valuable resource for anyone interested in learning more about NLP with Python.
Provides a practical guide to machine learning with Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning. This book would be a valuable resource for anyone interested in learning more about machine learning with Python.
Provides a comprehensive overview of machine learning with R. It covers the different types of machine learning algorithms, as well as the different R libraries that can be used for machine learning. This book would be a valuable resource for anyone interested in learning more about machine learning with R.
Save
Provides a comprehensive overview of deep learning. It covers the different types of deep learning algorithms, as well as the different applications of deep learning. This book would be a valuable resource for anyone interested in learning more about deep learning.
Provides a gentle introduction to machine learning with Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning. This book would be a valuable resource for anyone interested in learning more about machine learning with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7o08dd/fine
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Effort
2.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Focuses heavily on teaching students how to fine-tune BERT models, which is important for students looking to enter the field of Natural Language Processing (NLP)
Emphasizes practical implementation, guiding learners through the process of working with real-world data and utilizing TensorFlow and TensorFlow Hub to train and evaluate BERT models
Well-suited for learners with prior experience in Python, deep learning for NLP, and model training with TensorFlow or Keras API, catering to individuals with a strong technical foundation
Primarily targets learners based in the North America region, which may limit accessibility for individuals in other geographical locations
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