Coding Challenge #106: XOR Problem with TensorFlow.js

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Published on June 11, 2018 1:41:43 PM ● Video Link: https://www.youtube.com/watch?v=N3ZnNa01BPM

Duration: 51:47

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In this coding challenge, I visualize the outputs of a neural network solving XOR using TensorFlow.js. Code: https://thecodingtrain.com/challenges/106-xor-problem-with-tensorflowjs

🕹️ p5.js Web Editor Sketch: https://editor.p5js.org/codingtrain/sketches/ueNMC_Wh6

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References:
💻 TensorFlow.js: https://js.tensorflow.org/
💻 ml5.js: https://ml5js.org
💻 Adam optimizer: https://arxiv.org/abs/1412.6980

Videos:
🚂 TensorFlow.js playlist: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YIeVA3dNxbR9PYj4wV31oQ
🚂 ES6 Arrow Function: https://www.youtube.com/watch?v=mrYMzpbFz18
🚂 TensorFlow.js: Layers API: https://youtu.be/F4WWukTWoXY
🚂 async/await: https://youtu.be/XO77Fib9tSI
🔴 Coding Train Live 144: https://youtu.be/_wC4sn5qKQw?t=1885s


Related Coding Challenges:
🚂 #92 XOR Problem: https://youtu.be/188B6k_F9jU
🚂 #104 Linear Regression with TensorFlow.js: https://youtu.be/dLp10CFIvxI
🚂 #105 Polynomial Regression with TensorFlow.js: https://youtu.be/tIXDik5SGsI

Timestamps:
0:00 Introduction
3:43 Architecture of model
8:02 InputShape, units, and activation function
11:46 Optimizer and loss function
13:50 tf.train.sgd()
17:23 model.predict()
23:34 Input data
29:00 Train the model
36:54 Shuffle training dta
38:02 Memory management
40:08 Draw output
45:32 Train()

Editing by Mathieu Blanchette
Animations by Jason Heglund
Music from Epidemic Sound

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🔗 p5.js: https://p5js.org
🔗 p5.js Web Editor: https://editor.p5js.org/
🔗 Processing: https://processing.org

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#machinelearning #xorproblem #p5js #javascript #tensorflowjs