Intermediate Machine Learning: PyTorch Continued

Luddy School of Informatics, Computing, and Engineering, 700 N Woodlawn Avenue, Bloomington, 47404

Join our Google Developer Student Club for Applied Intermediate Machine Learning in PyTorch! Take your machine learning skills to the next level with Technical Lead Danishjeet Singh. Don't miss this opportunity to deepen your understanding. Register now!

Mar 22, 2023, 10:30 – 11:30 PM



Key Themes

Explore MLML Study JamMachine Learning

About this event

Are you ready to enhance your machine learning skills and dive deeper into the world of AI? Join our Google Developer Student Club for an immersive workshop on Applied Intermediate Machine Learning in PyTorch, taught by Technical Lead Danishjeet Singh. This workshop is a continuation of our previous event and is designed to take your machine learning knowledge to the next level.

During this interactive session, you'll build upon the foundations of machine learning and PyTorch to explore more advanced concepts and techniques. Danishjeet Singh will guide you through hands-on exercises and provide insights into solving real-world machine learning problems.

What to expect:

Recap of machine learning fundamentals: Refresh your understanding of core machine learning concepts, including data preprocessing, model evaluation, and training.

Advanced neural network architectures: Explore more complex neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and learn how to implement them using PyTorch.

Transfer learning and fine-tuning: Discover how to leverage pre-trained models and transfer learning to improve the performance of your machine learning models. Learn techniques for fine-tuning and adapting models to new tasks.

Handling unbalanced data and class imbalance: Gain insights into dealing with unbalanced datasets and class imbalance scenarios. Learn strategies for mitigating bias and improving model performance.

Hyperparameter tuning: Understand the importance of hyperparameters in machine learning models and learn techniques for optimizing them to achieve better performance.

Model deployment and productionization: Explore techniques for deploying trained models to production environments and integrating them into real-world applications.

Q&A and networking: Engage in a live Q&A session with Danishjeet Singh and connect with fellow participants. Share insights, seek clarification, and expand your professional network.

Prerequisite: This workshop is a continuation of our previous Introduction to Machine Learning Fundamentals in PyTorch workshop. Participants are expected to have a solid understanding of machine learning basics and PyTorch fundamentals.

Note: This workshop is suitable for participants with intermediate knowledge of machine learning and PyTorch. Prior experience with Python programming and familiarity with neural networks will be beneficial. Bring your laptop with your preferred code editor and a curious mindset. Register now to secure your spot and take your machine learning skills to the next level with Applied Intermediate Machine Learning in PyTorch!


  • Elizabeth Jiang

    GDSC Lead

  • Ayman Bolad

    Technical Lead

  • Danishjeet Singh

    Technical Lead

  • Matei Cloteaux

    Technical Lead

  • Aniedi Udo-Obong

    Regional Leader

  • Adarsh Vulli

    Event Coordinator

Contact Us