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Music and Art with Machine Learning | GDG DevFest Bangkok 2017 (Oct 7th, 2017)

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Virot "Ta" Chiraphadhanakul
Virot "Ta" Chiraphadhanakul

This talk discusses Google Brain's Magenta, a project using TensorFlow to generate art and music. The slides are adapted from Douglas Eck's talk at Google I/O 2017 (https://www.youtube.com/watch?v=2FAjQ6R_bf0)

Presentations & Public Speaking
1 of 49
Music and Art with Machine Learning Part I Ta Virot Chiraphadhanakul (@tvirot) Google Developer Expert in Machine Learning Managing Director, Skooldio
AI and Creativity
Make Music and Art Using Machine Learning https://magenta.tensorflow.org/
Deep Learning A Quick Intro
Photo: NVIDIA Blog
x1 + x2 = 0
http://playground.tensorflow.org/
http://playground.tensorflow.org/
DEMO https://teachablemachine.withgoogle.com/ Teachable Machine
Machine Learning on Google Cloud Platform Pre-trained ML models Custom ML models
Understand the content of images ● Label Detection ● Optical Character Recognition ● Explicit Content Detection ● Face Detection etc. Google Cloud Vision API Photos: Google Cloud Platform / Kaz Sato @tvirot
Generative Models Training a model to create things
"What an AI cannot create, it does not understand." — Ian Goodfellow, Google Brain
Style Transfer https://research.googleblog.com/2016/10/supercharging-style-transfer.html
https://experiments.withgoogle.com/ai/sound-maker/view/
https://deeplearnjs.org/demos/performance_rnn
Sketch-RNN A Generative Model for Sketch Drawings
https://quickdraw.withgoogle.com/
https://quickdraw.withgoogle.com/data
Autoencoders ● A neural network with the target values set to be equal to the inputs ● The goal is to learn an approximation to the identity function -- a lower-dimensional feature representation ● By placing constraints on the network, such as by limiting the number of hidden units, we can discover interesting structure about the data
Autoencoders Input OutputZEncoder Decoder Latent variable / Embedding Reconstructed Input
Sequence Models ● Data points are related or dependent on each other (e.g., text, audio waves, videos) ● Challenges: ○ Order matters ○ Statistical Invariance ○ Long-term dependencies
Recurrent Neural Networks Networks with loops http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Unfolded RNN http://colah.github.io/posts/2015-08-Understanding-LSTMs/
A Simple RNN http://colah.github.io/posts/2015-08-Understanding-LSTMs/
RNN Architectures http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Sketch-RNN Sequence-to-Sequence Variational Autoencoder ZEncoder Decoder A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Sketch-RNN Sequence-to-Sequence Variational Autoencoder Forward + Backward RNN Z Decoder A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Sketch-RNN Sequence-to-Sequence Variational Autoencoder Latent vector input + noise Encoder Decoder A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Sketch-RNN Sequence-to-Sequence Variational Autoencoder RNN + Mixture of Gaussians ZEncoder A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Sketch-RNN Sequence-to-Sequence Variational Autoencoder A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
https://github.com/tensorflow/magenta-demos/blob/master/jupyter-notebooks/Sketch_RNN.ipynb
Conditional Reconstructions from a model trained on cat sketches https://research.googleblog.com/2017/04/teaching-machines-to-draw.html
Conditional Reconstructions from a model trained on cat sketches A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Conditional Reconstructions from a model trained on pig sketches A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
DEMO https://magenta.tensorflow.org/assets/sketch_rnn_demo/multi_vae.html Variational Autoencoder
Latent Space Interpolation: how one image morphs into another? https://research.googleblog.com/2017/04/teaching-machines-to-draw.html
DEMO https://magenta.tensorflow.org/assets/sketch_rnn_demo/interp.html Sketch-RNN Interpolation
Latent Space Learned relationships between abstract concepts A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Predicting endings of incomplete sketches A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
DEMO https://magenta.tensorflow.org/assets/sketch_rnn_demo/multi_predict.html Sketch-RNN Predictor
Creative Applications
Pattern designs Generate a large number of similar, but unique designs A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Abstract designs A Neural Representation of Sketch Drawings (David Ha, Douglas Eck)
Thank you! #devfestbkk #machinelearning Ta Virot Chiraphadhanakul (@tvirot) Google Developer Expert in Machine Learning Managing Director, Skooldio

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