Deep Learning Boot Camp
Summer 2025
May 7, 2025
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Aug 15, 2025

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Registration Deadlines
May 1, 2025
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Erdős members / alumni who have successfully completed a prior Erdős Data Science Boot Camp Project
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Erdős members / alumni who have successfully completed a prior Erdős Data Science Boot Camp Project
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Category
Advance, Supplemental, Self-Directed, Project-Based, Boot Camp
Overview
Welcome to deep learning! 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 the end of the cohort.

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!
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 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:
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Textbook/Notes
Schedule
Click on any date for more details
Deep Learning Orientation
May 9, 2025 at 08:00 PM UTC
EVENT
Deep Learning Project Pitch Day
May 30, 2025 at 08:00 PM UTC
EVENT
Deep Learning Live Review
May 16, 2025 at 08:00 PM UTC
EVENT
Deep Learning Class Networking Event
Jun 6, 2025 at 08:00 PM UTC
EVENT
Deep Learning Live Review
May 23, 2025 at 08:00 PM UTC
EVENT
Deep Learning Project Showcase
Aug 15, 2025 at 04:00 PM UTC
EVENT
Please check your registration email for program schedule and zoom links.
Project/Homework Deadlines
Jun 13, 2025
09:00 PM UTC
Deep Learning Project Teams and Topic Due Date
Last chance to formalize teams
Aug 11, 2025
09:00 PM UTC
Deep Learning Final Project Due
Due date for project