W251 reloaded (Fall 2020)

The revised class is focused on Deep Learning and Big Data at the Edge and in the Cloud.

To follow the class, you'll a Mac or a PC (Windows or Linux) with an ability to run docker or VirtualBox VMs. You will also need additional equipment as follows:

Required equipment:

We are working on sourcing discount codes for the Jetson Xavier NX Developer Kit (@$329 in the US) and hope to have them available the week before classes start.
NB We have an update from Nvidia that we will get the discount codes to distribute early this week (of 17th August 2020), it is recommended to hold on for ordering till you get this code. This may mean the devices are recieved too late to complete the homeworks for the week 01 deadline next week, which is ok - there will be no penalty if you are a few days late due to the discount codes arriving late.

For details on set up see homework 1:

1. Jetson Xavier NX Developer Kit
2. MicroSD card (64GB minimum size)
3. USB MicroSD card reader
4. NVMe M.2 SSD (256GB minimum size) NOTE: SATA M.2 SSDs will not work
5. Size 0 Philips head screwdriver
6. Micro USB to USB cable
7. USB Webcam

Homeworks:

A homework is due before each class. There are two types of homeworks: graded and credit only. Here is the link to class 1 - be sure to complete the setup of your Xavier as described in homework 1

SSH Reminder:

Ensure that any VSI/VM create prohibts login with password prohibited. See: https://github.com/MIDS-scaling-up/v2/tree/master/week02/hw/README.md for details.

Graded homeworks

The graded homeworks are week 3, week 6, week 9, week 11

Final Projects (due in the final class session of the semester)

• Form teams of three to four people
• Leverage big data, cloud, DL, and the edge device to do something cool
• Should be more than you can do on a workstation
• To turn in: White paper explaining what you did, how you did it, what you learned, what went right (or wrong) and a brief presentation (10-15 minutes)

An example of a final project:

• Leverage a dataset of missing persons.
• Train a model in the cloud to recognize the missing persons.
• Deploy the model to your edge device.
• If a person is recognized, send the image and location back to the cloud for further actions.