Hands-on workshops covering some introductory Machine Learning tools and techniques.
- Python Tools for Machine Learning – February 10th
- Classes in Python, Deep Learning Frameworks – February 24th
- Training and Fine Tuning Models for Downstream Tasks – March 24th
- Reporting Your Findings in LaTeX – April 14th
February 10th, Python Tools for Machine Learning
The first in a series of workshops geared toward making Machine Learning more accessible to the community. In this first workshop, we will be covering useful tools and techniques such as NumPy, Tensors, Slicing, List comprehension, and Broadcasting.
This will lay the groundwork for future workshops where we will dive into more complex topics such as exploring ML frameworks, configuring data loaders, and creating & using ML models.
February 24th, Classes in Python & Deep Learning Frameworks
In the second workshop of the semster, we will begin diving into what makes Machine Learning models work. Topics covered will include Python classes, data loaders, and the PyTorch framework.
By the end of this session attendees will understand the various components of a Neural Network and have gained experience in developing a ML model to accomplish a specific task.
March 24th, Fine Tuning for Downstream Tasks
Building on results from the second workshop, our third workshop will delve into fine tuning a pre-built model for downstream tasks.
April 14th, Reporting Your Findings in LaTeX
In the fourth and final installation of this workshop series, we will discuss how to write a proper report using LaTeX.