Nets4Learning

Web platform for the design and execution of deep learning models for learning and initiation in the study of deep learning models.

The tool proposes different classical machine learning problems with known data sets to study and model different neural network architectures and training parameters. The tool addresses different examples of deep learning models such as tabular classification, image classifier or object identification.

There are some classical problems prepared and reviewed to make predictions, the tool has the feature to preprocess data sets that the user uploads, train models and predict in which class it would be classified.

Architecture Playground

MODELS

1. Tabular classification
   • MODEL_CAR.js
   • MODEL_IRIS.js
   • MODEL_LYMPHOGRAPHY.js
2. Linear regression
   • MODEL_1_SALARY.js
   • MODEL_2_AUTO_MPG.js
   • MODEL_3_BOSTON_HOUSING.js
   • MODEL_4_BREAST_CANCER.js
   • MODEL_5_STUDENT_PERFORMANCE.js
   • MODEL_6_WINE.js
3. Image classifier
   • MODEL_IMAGE_MNIST.js
   • MODEL_IMAGE_MOBILENET.js
4. Object identification
   • MODEL_FACE_DETECTOR.js
   • MODEL_FACE_MESH.js
   • MODEL_MOVE_NET_POSE_NET.js
   • MODEL_COCO_SSD.js

Install local

npm install

NODE_OPTIONS=--max-old-space-size=4096

Project environment variables

Create the files .env.development or .env.production.

HTTPS=false
WATCHPACK_POLLING=true
FAST_REFRESH=true
NODE_ENV="development"

PUBLIC_URL="http://localhost:3000/n4l"

REACT_APP_ENVIRONMENT="development"
REACT_APP_PATH="/n4l"
REACT_APP_PUBLIC_URL="http://localhost:3000/n4l"
REACT_APP_GA_MEASUREMENT_ID="G-XXXXXXXXXX"
REACT_APP_SHOW_NEW_FEATURE="true"
REACT_APP_NEW_FEATURE="linear-regression"

Development

node -v 
# v18.16.1
npm -v 
# 9.5.1

npm run start:development

Build

npm run build:production

Command matrix

	development	build
Development	npm run start:development
Production	npm run start:production	npm run build:production
simidat		npm run build:simidat