├── .gitignore ├── 1. GAN - theory and applications ├── 1. GAN - theory and applications.ipynb ├── README.md ├── images │ ├── conditional_mnist.png │ ├── mnist.gif │ └── pix2pix.png └── slides │ ├── beamercolorthememetropolis-highcontrast.sty │ ├── beamercolorthememetropolis.sty │ ├── beamerfontthememetropolis.sty │ ├── beamerinnerthememetropolis.sty │ ├── beamerouterthememetropolis.sty │ ├── beamerthememetropolis.sty │ ├── bibl.bib │ ├── images │ ├── GANs.png │ ├── SRGAN.png │ ├── celeb1024-results.jpg │ ├── cnn.pdf │ ├── cnn.svg │ ├── diagram.pdf │ ├── diagram.png │ ├── discriminator.jpg │ ├── euroscipy_logo.png │ ├── im2im.jpg │ ├── lecun3.jpg │ ├── loss_function.pdf │ ├── loss_function.tex │ ├── loss_function_1.pdf │ ├── loss_function_1.tex │ ├── nn.png │ ├── nn.svg │ ├── pycon_x_logo.png │ ├── pycon_x_logo.svg │ ├── recurrent.png │ ├── spade.jpg │ ├── spade │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── stylegan │ │ ├── 099000.png │ │ ├── 099001.png │ │ ├── 099002.png │ │ ├── 099003.png │ │ ├── 099004.png │ │ ├── 099005.png │ │ ├── 099006.png │ │ ├── 099007.png │ │ ├── 099008.png │ │ ├── 099009.png │ │ ├── 099010.png │ │ ├── out.avi │ │ └── out.mp4 │ ├── teaser_high_res.jpg │ ├── teaser_v3.jpg │ ├── training_meme.jpg │ ├── treepond.avi │ ├── treepond.gif │ ├── treepond.mp4 │ ├── treepond.png │ ├── zuru-logo.png │ └── zuru.png │ ├── mydinat.bst │ ├── pgfplotsthemetol.sty │ ├── slides-adobe.pdf │ ├── slides-adobe.tex │ ├── slides.pdf │ ├── slides.pdfpc │ ├── slides.tex │ └── svg-inkscape │ ├── cnn_svg-tex.pdf │ ├── cnn_svg-tex.pdf_tex │ ├── nn_svg-tex.pdf │ ├── nn_svg-tex.pdf_tex │ ├── pycon_x_logo_svg-tex.pdf │ └── pycon_x_logo_svg-tex.pdf_tex ├── 2. GANs in Tensorflow ├── 2.1. Writing a GAN from scratch.ipynb ├── 2.BONUS - Conditional GAN from scratch.ipynb ├── gif │ └── learning_gaussian.gif └── images │ ├── DCGAN_G.png │ └── dataset-estimator.jpg ├── 3. AshPy ├── 3.1. AshPy and TensorFlow Datasets.ipynb └── images │ ├── dataset-ashpy.png │ ├── dcgan.png │ ├── dcgan_discriminator.png │ ├── dcgan_generator.png │ └── deconvolution.png ├── 4. Production ├── 4.1 Serving Models using TF 2.0 and Cloud Functions.ipynb ├── client │ ├── css │ │ └── style.css │ ├── index.html │ ├── interpolation.html │ └── js │ │ ├── interpolate.js │ │ ├── main.js │ │ └── model.js ├── deploy │ ├── .gcloudignore │ ├── main.py │ ├── requirements.in │ ├── requirements.txt │ └── tests.py └── images │ ├── differences-cloud-ai.png │ ├── google-cloud-ml-logo.png │ ├── google-cloud.png │ ├── testing.png │ └── tf-versions-ml-engine.png ├── LICENSE.md ├── README.md ├── images └── logo.jpg ├── requirements.txt └── runtime.txt /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zurutech/gans-from-theory-to-production/HEAD/.gitignore -------------------------------------------------------------------------------- /1. GAN - theory and applications/1. 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