Deep Learning Boot Camp
May-Summer 2024
Jun 7, 2024
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Aug 29, 2024
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Registration Deadlines
Jun 8, 2024
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Erdős members / alumni who have successfully completed a prior Erdős Data Science Project
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Category
Advance, Supplemental, Self-Directed, Mini-Course
Overview
This is a semi-structured deep learning boot camp. Each week, you'll complete assigned readings from 2 deep learning books. During the first few weeks, there will be weekly meetings with the instructors and all attendees on Zoom. As you progress more into the material and your projects, you will meet according to your group schedule.
In order to receive a deep learning certificate, you must submit a (team-based) final project by **August 29, 2024**.
Click here to be invited to the slack organization: The Erdős Institute
Click here to access the slack channel: #slack-channel
Organizers, Instructors, and Advisors
Lindsay Warrenburg
Associate Director of Erdős
Office Hours:
as needed
Email:
Preferred Contact:
Slack
Slack is the best way to contact me!
Marcos Ortiz
Lead Deep Learning TA
Office Hours:
Fridays, 11:45-12 PM ET
Email:
Preferred Contact:
Slack
Objectives
- Learn the basics of deep learning
- Understand how deep learning is used in industry
- Feel comfortable with deep learning code (PyTorch and FastAI)
Project Examples
TEAM 9
RivusVox Editor
Zachary Bezemek,Francesca Balestrieri
RivusVox Editor: the world's first near-live zero-shot adaptive speech editing system
TEAM 3
Taxi Demand Forecasting
Ngoc Nguyen, Li Meng, Sriram Raghunath, Nazanin Komeilizadeh, Noah Gillespie, Edward Ramirez
Knowing where to go to find customers is the most important question for taxi drivers and ride hailing networks. If demand for taxis can be reliably predicted in real-time, taxi companies can dispatch drivers in a timely manner and drivers can optimize their route decision to maximize their earnings in a given day. Consequently, customers will likely receive more reliable service with shorter wait time. This project aims to use rich trip-level data from the NYC Taxi and Limousine Commission to construct time-series taxi rides data for 63 taxi zones in Manhattan and forecast demand for rides. We will explore deep learning models for time series, including Multilayer Perceptrons, LSTM, Temporal Graph-based Neural Networks, and compare them with a baseline statistical model ARIMAX.
First Steps/Prerequisites
Program Content
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Course materials are available on github through the following link:
github message for user
Textbook/Notes
Deep Learning Orientation
Course Overview
Structure, pre-requisites, timeline
Project/Homework Instructions
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Schedule
Click on any date for more details
Deep Learning Orientation
Jun 7, 2024 at 04:00 PM UTC
EVENT
Deep Learning Project Pitch Day
Jun 28, 2024 at 04:00 PM UTC
EVENT
Deep Learning Week 1 Review
Jun 14, 2024 at 04:00 PM UTC
EVENT
Deep Learning Networking Event
Jul 5, 2024 at 04:00 PM UTC
EVENT
Deep Learning Week 2 Review
Jun 21, 2024 at 04:00 PM UTC
EVENT
Please check your registration email for program schedule and zoom links.
Project/Homework Deadlines
Jul 12, 2024
09:00 PM UTC
Deep Learning Project Topics / Team Deadline
You must decide on your team and project topic by this date
Aug 29, 2024
09:00 PM UTC
Deep Learning Final Project Deadline
To get a certificate, you must submit a project