ASAP_other_notebooks_plan.ipynb

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    "# Anomaly Screening Applying Pseudo-wells (ASAP)\n",
    "\n",
    "Stavanger Machine Learning Hackathon 18-19 September, 2018.\n",
    "\n",
    "Project page: <https://events.agilescientific.com/project/asap>\n",
    "\n",
    "## assembling Lukas' notebooks\n",
    "\n",
    "\n",
    "* ASAP_3 = seismic data loading and preprocessing: `Far Stack Preprocessing.ipynb`, `Near Stack Preprocessing.ipynb` to create the flattened horizon dataset.\n",
    "\n",
    "* ASAP_4 = unsupervised cluster `Unsupervised Cluster Analysis of Glitne Near-Far Offset Data and Fluid Factor.ipynb`\n",
    "\n",
    "* ASAP_5: UMAP analysis, VAE (Variational Autoencoder): `UMAP Analysis and Fluid Factor.ipynb`, `AVO-VAE-Step49.ipynb` to perform analysis of Variational Autoencoder Output. To create Autoencoder architecture use `VAE Architecture Test.ipynb`. Running training VAE on preprocessed data: `train.py`. Model trained using `vae.py` results output by using `vae_synthetics.py`. (Epoch used was epoch 49. Ran with default settings.)\n",
    "\n",
    "Original code for VAE modified form https://github.com/wanglouis49/pytorch-autoencoders/blob/master/conv_vae.py.\n",
    "\n",
    "\n",
    "\n",
    "## To-do\n",
    "\n",
    "* (...)"
   ]
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