Workshop – Deep Learning in Practice: A Hands-On Introduction

Workshop Description

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. It’s a hands-on class; you’ll learn to implement and understand both deep neural networks as well as unsupervised techniques using PyTorch, TensorFlow, Keras, and Python. Just as importantly, you’ll learn exactly what types of problems are appropriate for deep learning techniques, and what types of problems are not well suited to deep learning.

The instructors, Prerna and Bardia, take part in applying cutting-edge Large Language Models and other custom models to address various industries’ needs. They will be sharing case studies and examples from their experience during the workshop. Workshop participants will access much of the same state-of-the-art training material used for this work at Microsoft. Along the way, James will cover case studies detailing large-scale deployments for their internal clients that have generated astounding ROIs.

During the day, workshop attendees will gain the following practical hands-on experience:

• How to prepare, normalize, and encode data for deep learning systems.
• How to install deep learning libraries including TensorFlow, Keras, and PyTorch, and the pros and cons of each library.
• How to create deep learning predictive systems for various kinds of data: classical business data, time series data (such as sales data), image data (such as the famous MNIST dataset for handwriting recognition), and text/document data (such as legal contracts). These datasets are a great place to start – however, for the more experienced attendee, even more challenging, “next level” datasets, such as for object recognition, will be optionally available.

This workshop assumes you have a basic knowledge of machine learning terminology but does not assume you are a machine learning expert. Some theory will be presented but only enough to help you understand how to make a practical, working deep learning system. This is a code-based workshop, so some programming experience will be helpful. However, beginners will be able to follow along but may have to work a bit harder to keep up.

Instructors:

Bardia Beigi, Applied Scientist II, Microsoft

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master’s degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Prerna Singh, Applied Scientist II, Microsoft

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master’s degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.