Project slides: https://docs.google.com/presentation/d/1vHpVspekgYUq3bSH0ZFgR2Dli7UaHDsnwj2GMwaphk8/edit#slide=id.p

Self introduction: https://docs.google.com/document/d/1bW_yhEZyDqlGOP2iVZ83t46H7oCBtpkYToQHZUQy9LI/edit?usp=sharing

Transfer Learning tutorial: https://www.tensorflow.org/tutorials/images/transfer_learning

Status Updates

https://drive.google.com/drive/folders/1TKlf7CwJW2E3SlkJ_l_0EM2dCeqeSzKQ?usp=sharing

GitHub Repos

• https://github.com/OnlineCoder5467/Senior-Cap_ServerClient-
• https://github.com/Chaoward/Senior-Cap_WebClient
• https://github.com/kevinmaravillas/MobileClient
• https://github.com/Chaoward/MlNDE_Server

Teams

• Server: Johnson, Alvin, Sanjog, Jonathan, Xavier
• Web client: Howard, Nisapat
• Mobile client: Justin, Wilson, Enrico, Kevin

1/26/2023

 

11/17/2023

Is MobileNet V2 a already trained model?

Two parts: what's the different between 1st and 2nd part?

One more meeting on Friday 12/8

11/3/2023

Import Keras model in Tensor.js: https://www.tensorflow.org/js/tutorials/conversion/import_keras

Week 11: 10/30 - 11/3

Week 12: 11/6 - 11/10, Veterans Day (Friday 11/10)

Week 13: 11/13 - 11/17

Week 14: 11/20 - 11/24, Thanksgiving (Thursday 11/23), Fall Recess

Week 15: 11/27 - 12/1: Project Presentations (Friday 12/1)

Week 16: 12/4 - 12/8: Project documentation & deliverables due (Friday 12/8)

Week 17: Finals

10/31/2023

[Deliverables and Deadlines]

• Friday 11/3: Writing Test
• Friday 12/1: Presentation. See schedule
• Friday 12/8: Software Requirements Doc (SRD), preliminary version of Software Design Doc (SDD)

[Project name]

It's a project name, not an app name, so acronym is probably the way to go, e.g. Machine Learning for Network-Denied Environments (MLNDE). Try massaging the words to get a good acronym (e.g. MIND or MINDIE or something).

[Project description]

Project description is not a status report. Describe

• Problem - ML is widely used but rely on server/cloud resources
• Goal - develop ML algorithms/processes/systems that work in disconnected environments, and update when network is available.
• System - a prototype that demonstrates the feasibility of such algorithms/processes/systems. It consists of server, web client, mobile client ... 

Example: https://ascent.cysun.org/project/project/view/136

[Who's doing what]

Server

• Johnson: stuck setting up server side
• Alvin: model & project description
• Sanjog: demo on Friday
• Jonathan
• Xavier: project description; set up signin/signout routes for re-training the model

Alvin, Sanjog, Xavier: trying setting up the server code; see me if you can't get it to work (or let me know if you could get it to work)

Johnson: create a good demo of the Recognizing Objects model

Web client

• Howard: communicate with web api
• Nisapat: image gallery

* Do better with the use of MaterialUI library; more functions in addtion to the gallery

Mobile client

• Justin: UI of mobile client, custom labels
• Wilson: image classification; camera
• Enrico: communicate with web api
• Kevin: authentication

* Loading a model from file instead of importing a packaged model

Team change? - remove Johnson from web client and move a member from server to web client

People who have never spoke during weekly meetings should present on Friday: Sanjog, Xavier

[Data Science Class Progress]

KNN, Linear regression, random forest (decision tree), cross validation, logistic regression, using Python libraries

Neural network or deep learning?

[Technical hurdles]

• Set up server code (tomorrow/Wednesday 4pm I'll send Johnson a Zoom link)
• Switch front and back camera in mobile code (tabled)
• combine server web api code with model training code
• On mobile client, run a model loaded from a file (instead of a model packaged in tensorflow library)
  • For example, a file with .h5 suffix
  • As oppose to say "import * from @tensor-models"

[Overall design]

* Pick a model

• Recognizing digits (not this one)
• Dog or cat (web client) (not a model to begin with, just an example)
• Recognizing objects (training with a given set of labels) -- adds the flavor of re-training (with more labels)
• Dog species (mobilenet)

Friday demo of this model:

• Train this model with a set of label, e.g. {dog, cat, horse}
• Test this model with pictures of dog, cat, horse, and people
• Re-train with an additional label {person} and additional pictures of people
• Test this model again with picture of people.

10/20/2023

Johnson: model - file format and size

Kevin: try out Tensor.js

Howard and Nisapat: web client - should use a component library - Material UI

Kevin: registration with AWS

Jonathan: updated endpoints for text labels & confidence score ...

Johnson + Sanjog + Jonathan: add model training/re-training code to server side

Overall app design next week

10/13/2023

Johnson: model

Jonathon: set up demo server; images hosted on s3

Howard: web client; able to connect to server

Justin: mobile client; able to upload image to server

Model data format; run model on mobile; re-train model on server; overall app design (will meet in person to discuss); share GitHub repo info

9/29/2023

Attendees: Sanjog Baniya (late)

Sub-team leads:

• Server: Johnson
• Web client: Howard
• Mobile client: Kevin, Justin

Students taking the data science class: Kevin, Johnson, Jonathan, Justin, Sanjog, Alvin

Share Resources doc

9/19/2023

Attendees: Sanjog Baniya, Jonathon M Dooley, Enrico Efendi, Wilson Gan, Xavier Lara, Kevin Maravillas, Howard Nguyen, Nisapat Poolkwan, Johnson Tan, Justin To, Alvin Yu

[General Q&A]

[System Architecture]

1. Mobile client

• take a picture or upload a picture
• classify content to be cat or dog offline (need a model on device, need a library to run the picture through the model)
• When online (i.e., with network)
  • Connect to a server
  • Download an updated model (or a model update)
  • Upload any new user data to server (i.e., new pictures, and new tags -  predicted and user-entered)
• When connected with another mobile client
  • Shared the updated model / model update with the other client
  • The two clients will exchange new data (it could be one direction, or both, and it could be automatic, or specified by user)

2. Server

• has training data, and new data, and a model to begin with
• Provide a web API for clients to
  • Download a updated model / model updates
  • Upload new data
  • Train a new model with additional labeled data
  • Probably also need API for other operations

3. Web client (basically a user interface to manage the server)

• Display the list of different versions of the model
• Display the list of pictures and their labels
• Let user to label pictures for training purpose
• Issue a command to the server to retrain the model with newly labelled data
• ...

[Sample Application]

Image classification is probably a lot easier to implement and demo than dialog-based AI tutoring.

For example, an mobile app will allow a user to take a picture or upload an image, the app tells the user if the picture is a pic of a cat or a dog.

[Components and Sub-teams]

1. Server

Knowledge needed: Python, Flask, Web API, AI/MI

Team: Johnson, Alvin, Sanjog, Jonathan, Xavier

2. Web Client (for admin UI)

Knowledge needed: JavaScript/Node.js, React

Team: Howard, Johnson, Nisapat

3. Mobile Client

Knowledge needed: JavaScript/Node.js, React Native, AI/MI

Team: Justin, Wilson, Enrico, Kevin,

4. Peer Relay - somebody from the mobile client team do some research on this

Investigate mobile-to-mobile device communication technologies and libraries

[Collaboration] (discuss within team & subteams)

Ways/tools of communication, additional meeting time

[Tasks for near term]

• Select a model - for image classification, not too large, no GPU requirement, preferably can be incrementally updated (need to discuss this with Dr. Nye and Dr. Core on Friday)
• Come up with a functional design - what functions each system component should have (see the System Architecture part for starters)
• Initial setup and implementation
  • Set up development environment and GitHub repositories
  • Create three components (server, web client, mobile client) that can talk to each other
• Update project page on Ascent: project title and description
• Create a shared Resources page where team members can find project resources to learn/use (like the section below)

[Resources]

General

• O'Reilly Digital Library (a.k.a. Safari Books Online) - https://www.oreilly.com/library-access/?email=
  • Select "Institution not listed"
  • Enter your Cal State LA email

Machine Learning (MI) Basics (e.g. classification, labelling, model, training)

Python

• Python Tutorial
• Python Crush Course (3rd Ed)

Web API in Python

• Microservices APIs (on O'Reilly)

MI in Python

React

React Native

9/15/2023

opentutor.info

web-dev.pal3.org