
Coding Challenge #158: Shape Classifier Neural Network with ml5.js
In this challenge, I demonstrate the entire process of training and deploying a machine learning classification model in JavaScript -- data collection, model training, and prediction! π» Code: https://thecodingtrain.com/challenges/158-shape-classifier
π₯ ml5.js: DoodleNet: https://youtu.be/ABN_DWnM5GQ
Links discussed in this video:
π Processing: https://processing.org/
π p5.js Web Editor: https://editor.p5js.org/
π ml5.js Loading a pre trained model: https://learn.ml5js.org/#/reference/neural-network?id=loading-a-pre-trained-model
π p5.js copy() reference: https://p5js.org/reference/#/p5.Image/copy
π DoodleNet (GitHub): https://github.com/yining1023/doodleNet
Other videos mentioned in this video:
π₯ Workflow Series: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6Zu_uqEA6NqhLzKLACwU74X
π₯ ml5.js: Training a CNN for Image Classification: https://youtu.be/hWurN0XhzLY
π₯ ml5.js: Train a Neural Network with Pixels as Input: https://youtu.be/UaKab6h9Z0I
π₯ ml5.js: Train Your Own Neural Network: https://youtu.be/8HEgeAbYphA
π₯ ml5.js: What is a Convolutional Neural Network?: https://youtu.be/qPKsVAI_W6M
π₯ ml5.js: Save Neural Network Trained Model: https://youtu.be/wUrg9Hjkhg0
π₯ Quick, Draw! - Coding Challenge #122: https://youtu.be/yLuk0twx8Hc
Timestamps:
0:00 Introduction
1:33 Generating the dataset in Processing
5:50 Loading images in p5.js
8:34 Create a ml5 neural network
10:54 Adding the data
12:39 About training the model
13:53 Test training
15:21 Training with full dataset
16:44 Improving the dataset
18:20 Saved model
19:01 Separate sketch for prediction
19:40 Loading the model
21:13 Testing the model with shapes drawn in p5.js
24:41 Mouse drawn shapes
31:01 Using a webcam and a notebook
33:49 Wrap up
34:33 What's next?
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π₯ Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH
π₯ Intro to Programming: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6Zy51Q-x9tMWIv9cueOFTFA
π p5.js: https://p5js.org
π p5.js Web Editor: https://editor.p5js.org/
π Processing: https://processing.org
π Code of Conduct: https://github.com/CodingTrain/Code-of-Conduct
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