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Deep Learning Boot Camp

May-Summer 2024

Jun 7, 2024

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Aug 29, 2024

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To access the program content, you must first create an account and member profile and be logged in.

You are registered for this program.

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**.

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:

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

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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 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

Deep Learning Orientation

Course Overview

Structure, pre-requisites, timeline

Slides
Transcript
Code

Project/Homework Instructions

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Project/Team Formation
Project Submission
Projects README

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

©2017-2025 by The Erdős Institute.

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