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Deep Learning Boot Camp

Spring 2024

Feb 2, 2024

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May 3, 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

May 4, 2024

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Erdős members / alumni who have completed a prior Erdős Data Science Project

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Category

Advance, Supplemental, Self-Directed, Mini-Course

Overview

This is a self-paced deep learning boot camp, using the FastAI book as the foundation (http://course.fast.ai). It is suggested you take 12-15 weeks to go through the material. If possible, you should meet with others to have a weekly discussion group on the material.

In order to receive a deep learning certificate, you must submit a (team-based) final project by **May 03, 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

Lead Instructor

Office Hours:

as needed

Email:

Preferred Contact:

Slack

Participants should feel free to Slack me with any questions or comments!

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 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

github message for user

Program Content

Textbook/Notes

DataBoard

Demo for generating synthetic data

Jim Schwoebel, Engineering Manager at Verily, shows his new product. If you are interested in generating your own synthetic dataset for your project, then please contact Jim and Roman on slack.

Slides
Transcript
Code

Aware

Corporate Project Description

Jason Morgan, VP of Aware, discusses the problem and possible solutions to get started. Slides button for project description. Code button for dataset.

Transcript

Orientation

Introduction

Lindsay Warrenburg introduces the structure of this advanced Deep Learning Course.

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

Feb 2, 2024 at 05:00 PM UTC

EVENT

Aware Corporate Challenge Introduction

Mar 1, 2024 at 08:00 PM UTC

EVENT

Please check your registration email for program schedule and zoom links.

Project/Homework Deadlines

Feb 2, 2024

10:00 PM UTC

Deep Learning Registration Form

This is used to gain access to Slack / Github and to help find teams in similar time zones

Feb 9, 2024

02:34 PM UTC

Deep Learning Group Formation

This is your study group team and your project team

May 3, 2024

09:00 PM UTC

Deep Learning Final Project Due

Click on this box to submit project

©2017-2025 by The Erdős Institute.

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