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Deep Learning Boot Camp

Summer 2025

May 7, 2025

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Aug 15, 2025

Notes: You must have previously completed the Erdős Institute Data Science Boot Camp or pass our Assessment in order to register. 
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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.

Slack

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

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Lindsay Warrenburg

Associate Director of Erdős

Office Hours:

As Needed

Email:

Preferred Contact:

Slack

Slack is the best way to contact me!

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Marcos Ortiz

Lead Deep Learning TA

Office Hours:

As Needed

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

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github URL

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

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github URL

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

Participants must have successfully completed the data science bootcamp or pass a data science assessment before taking this course.
First Steps

Program Content

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Course materials are available on github through the following link:

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Request Access to GitHub

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Program Content

Textbook/Notes

Project Instructions

Instructions

Showing how to create a team, submit a project, and find the previous project database

Slides
Transcript

Project/Homework Instructions

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Project/Team Formation
Project Submission
Projects README

Project Instructions

Instructions

Showing how to create a team, submit a project, and find the previous project database

Slides
Transcript

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

©2017-2025 by The Erdős Institute.

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