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Fine-tune a transformer-based neural network with PyTorch

IntermediateGuided Project

Master the art of fine-tuning a transformer-based neural network using PyTorch. Discover the power of transfer learning as you meticulously fine-tune the entire neural network, comparing it to the more focused approach of fine-tuning just the final layer. Unlock this essential skill by immersing yourself in this end-to-end hands-on project today!

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 60

Skills You Will Learn

  • Artificial Intelligence, Generative AI, Python, Deep Learning, Natural Language Processing, PyTorch

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • March 13, 2025
About this Guided Project

A look at the project ahead

Imagine that you have a classification task, and you want to solve it by using a transformer-based neural network model. Here are your options:
  1. Train a model from scratch: One approach is to train a new model entirely from scratch. However, this method might not be the most effective. When you start from scratch, you miss out on the opportunity to benefit from transfer learning.
  2. Fine-tune a pretrained model: Transfer learning involves repurposing a pretrained model that was initially trained for a different task. By fine-tuning this pretrained model, you can adapt it to your specific classification task.
In this hands-on project, you gain a comprehensive understanding of the entire end-to-end pipeline for fine-tuning a transformer-based neural network.

Learning objectives

Upon completion of this project, you have the ability to:
  • Define and pretrain a transformer-based neural network using PyTorch for a classification task.
  • Fully fine-tune the pretrained model for a different classification task.
  • Compare results by fine-tuning only the last layer of the pretrained model.

What you'll need

For this project, you need an intermediate level of proficiency in Python, PyTorch, and deep learning. Additionally, the only equipment that you need is a computer equipped with a modern browser, such as the latest versions of Chrome, Edge, Firefox, or Safari.

Instructors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Ashutosh Sagar

Data Scientist

I am currently a Data Scientist at IBM with a Master’s degree in Computer Science from Dalhousie University. I specialize in natural language processing, particularly in semantic similarity search, and have a strong background in working with advanced AI models and technologies.

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