{
  "capped": false,
  "live": 208,
  "meta": 213,
  "kernels": [
    {
      "ref": "fakhretdinovarthur/notebook4c602f896c",
      "title": "notebook4c602f896c",
      "source": "live",
      "kernel_id": "111236051"
    },
    {
      "ref": "eladsim/iceberg-vs-ship",
      "title": "iceberg vs ship",
      "source": "live",
      "kernel_id": "103376029"
    },
    {
      "ref": "alexagomez218/notebook833183d5ca",
      "title": "notebook833183d5ca",
      "source": "live",
      "kernel_id": "103234674"
    },
    {
      "ref": "nocarbonintelligence/isar-classification",
      "title": "ISAR - classification",
      "source": "live",
      "kernel_id": "103233737"
    },
    {
      "ref": "georgeokello/statoil-c-core-iceberg-classifier-challenge",
      "title": "Statoil/C-CORE Iceberg Classifier Challenge",
      "source": "live",
      "kernel_id": "90092456"
    },
    {
      "ref": "alperugurca/statoil-c-core-iceberg-classifier-challenge",
      "title": "Statoil-C-CORE-Iceberg-Classifier-Challenge",
      "source": "live",
      "kernel_id": "69441179"
    },
    {
      "ref": "suchadapleasong/iceberg2",
      "title": "Iceberg2",
      "source": "live",
      "kernel_id": "53721167"
    },
    {
      "ref": "suchadapleasong/iceberg",
      "title": "Iceberg",
      "source": "live",
      "kernel_id": "53570268"
    },
    {
      "ref": "hanpil/240321-keras-model-for-beginners-eda-r-d",
      "title": "240321_Keras Model for Beginners+EDA+R&D",
      "source": "live",
      "kernel_id": "53285444"
    },
    {
      "ref": "jihunhyeon/5-statoil-c-core-iceberg-classifier-challenge",
      "title": "[5] Statoil/C-CORE Iceberg Classifier Challenge",
      "source": "live",
      "kernel_id": "53080861"
    },
    {
      "ref": "yeowonn/img-iceberg-sol-4",
      "title": "img iceberg_sol_4",
      "source": "live",
      "kernel_id": "53050152"
    },
    {
      "ref": "yueunchoi/240321-keras-model-for-beginners-eda-r-d",
      "title": "240321_Keras Model for Beginners+EDA+R&D",
      "source": "live",
      "kernel_id": "52990039"
    },
    {
      "ref": "yeowonn/img-iceberg-sol-3",
      "title": "img iceberg_sol_3",
      "source": "live",
      "kernel_id": "52699848"
    },
    {
      "ref": "yeowonn/img-iceberg-sol-2",
      "title": "img iceberg_sol_2",
      "source": "live",
      "kernel_id": "52645479"
    },
    {
      "ref": "yeowonn/img-iceberg-sol-1",
      "title": "img iceberg_sol_1",
      "source": "live",
      "kernel_id": "52211546"
    },
    {
      "ref": "pailinsapphire/notebookd855c2b920",
      "title": "notebookd855c2b920",
      "source": "live",
      "kernel_id": "51960770"
    },
    {
      "ref": "shaileshshettyd/iceberg-inferno-3000",
      "title": "Iceberg_Inferno_3000",
      "source": "live",
      "kernel_id": "51417728"
    },
    {
      "ref": "oscarbeijbom/iceberg-data-visualizer",
      "title": "Iceberg data visualizer",
      "source": "live",
      "kernel_id": "43730179"
    },
    {
      "ref": "pumpkinpiecake/notebook75197ada99",
      "title": "notebook75197ada99",
      "source": "live",
      "kernel_id": "37150416"
    },
    {
      "ref": "semjinwu/statoil-c-core-iceberg-classifier-challenge",
      "title": "Statoil/C-CORE Iceberg Classifier Challenge",
      "source": "live",
      "kernel_id": "33466911"
    },
    {
      "ref": "masoumehavahedi/glcm-features-lgbm-image-classification",
      "title": "GLCM features & LGBM Image Classification",
      "source": "live",
      "kernel_id": "16518194"
    },
    {
      "ref": "sh0wmaker/binary-image-classification-keras-for-lecture",
      "title": "📔Binary Image Classification_Keras for lecture",
      "source": "live",
      "kernel_id": "15912759"
    },
    {
      "ref": "sh0wmaker/binary-image-classification",
      "title": "Binary image classification한글필사",
      "source": "live",
      "kernel_id": "15843627"
    },
    {
      "ref": "oscarsommervold/iceberg-challenge-exploration",
      "title": "iceberg-challenge exploration",
      "source": "live",
      "kernel_id": "15693211"
    },
    {
      "ref": "leeyongsung/notebook028c10f49c",
      "title": "notebook028c10f49c",
      "source": "live",
      "kernel_id": "12524466"
    },
    {
      "ref": "bwahns/iceberg-classifier-eric",
      "title": "Iceberg Classifier-Eric",
      "source": "live",
      "kernel_id": "8403827"
    },
    {
      "ref": "anil777/transfer-learning-with-vgg-16-cnn-aug-lb-0-51be84",
      "title": "Transfer Learning with VGG-16 CNN+AUG LB 0. 51be84",
      "source": "live",
      "kernel_id": "3611020"
    },
    {
      "ref": "anil777/transfer-learning-with-vgg-16-cnn-aug-lb-0-1712",
      "title": "Transfer Learning with VGG-16 CNN+AUG LB 0.1712",
      "source": "live",
      "kernel_id": "3610997"
    },
    {
      "ref": "twhitehurst3/fcn-keras-iceberg",
      "title": "FCN Keras Iceberg",
      "source": "live",
      "kernel_id": "2924804"
    },
    {
      "ref": "youssefmubarak/statoil-iceberg-classifier",
      "title": "Statoil: Iceberg_Classifier",
      "source": "live",
      "kernel_id": "1722160"
    },
    {
      "ref": "poigal/transfer-learning-feature-extraction-and-pca",
      "title": "Transfer Learning Feature extraction and PCA",
      "source": "live",
      "kernel_id": "1385911"
    },
    {
      "ref": "rohith007/ice-berg-classifier-challenge",
      "title": "Ice Berg Classifier Challenge",
      "source": "live",
      "kernel_id": "1083946"
    },
    {
      "ref": "jihyeseo/image-iceberg-classify",
      "title": "image iceberg classify",
      "source": "live",
      "kernel_id": "958595"
    },
    {
      "ref": "nafisur/iceberg-classifier-multipleinput-withdatagen",
      "title": "Iceberg_Classifier_multipleinput_withdatagen",
      "source": "live",
      "kernel_id": "940469"
    },
    {
      "ref": "nafisur/iceberg-classifier-multipleinput-nodatagen",
      "title": "Iceberg_Classifier_multipleinput_nodatagen",
      "source": "live",
      "kernel_id": "940442"
    },
    {
      "ref": "san7911/icebergtest",
      "title": "IcebergTest",
      "source": "live",
      "kernel_id": "905991"
    },
    {
      "ref": "san7911/keras-cnn-statoil-iceberg-lb-0-1995-now-0-1516",
      "title": "Keras CNN - StatOil Iceberg LB 0.1995 (now 0.1516)",
      "source": "live",
      "kernel_id": "901901"
    },
    {
      "ref": "mukeshiitr/explore-stacking-another-hi-lo-and-clip-probs",
      "title": "Kerneld3f385e79b",
      "source": "live",
      "kernel_id": "649242"
    },
    {
      "ref": "nikhilpandey360/fork-of-statoil-final-try",
      "title": "Fork of Statoil final try",
      "source": "live",
      "kernel_id": "582370"
    },
    {
      "ref": "pkolebski/cnn-data-augentation-0-1978",
      "title": "CNN + data augentation",
      "source": "live",
      "kernel_id": "580920"
    },
    {
      "ref": "zeeshan1994/convolutional-autoencoders",
      "title": "Convolutional autoencoders",
      "source": "live",
      "kernel_id": "575811"
    },
    {
      "ref": "nikhilpandey360/convolutional-neural-net-tutorial-tensorflow",
      "title": "Convolutional Neural Net Tutorial (tensorflow) ",
      "source": "live",
      "kernel_id": "570118"
    },
    {
      "ref": "gaborfodor/fix-cv-folds-based-on-inc-angle",
      "title": "Fix cv folds based on inc_angle",
      "source": "live",
      "kernel_id": "568218"
    },
    {
      "ref": "azakhtyamov/simple-logreg-with-only-3-leaky-features",
      "title": "Simple LogReg with only 3 leaky features",
      "source": "live",
      "kernel_id": "564445"
    },
    {
      "ref": "kleberbernardo/iceberg-or-not",
      "title": "Iceberg or not?",
      "source": "live",
      "kernel_id": "560540"
    },
    {
      "ref": "kjeanclaude/end-to-end-iceberg-classificationlightgbm-lb0-342",
      "title": "End-to-End-Iceberg-ClassificationLightGBM-LB0.342",
      "source": "live",
      "kernel_id": "559143"
    },
    {
      "ref": "farazramtin/support-vector-machine-for-beginners-roc-auc-92",
      "title": "Support Vector Machine for Beginners [roc_auc=92%]",
      "source": "live",
      "kernel_id": "557530"
    },
    {
      "ref": "tomcwalker/2-pass-convnet-with-sensor-fusion-data-lb0-177",
      "title": "2 pass convnet with sensor fusion data (lb0.177)",
      "source": "live",
      "kernel_id": "553874"
    },
    {
      "ref": "varunkashyapks/basic-keras-model-and-visualization",
      "title": "Basic Keras Model and Visualization",
      "source": "live",
      "kernel_id": "553579"
    },
    {
      "ref": "mpware/features-engineering-lightgbm",
      "title": "Features Engineering + LightGBM",
      "source": "live",
      "kernel_id": "550705"
    },
    {
      "ref": "chechir/danger-with-different-weights-and-clip",
      "title": "Danger with different weights and clip",
      "source": "live",
      "kernel_id": "549940"
    },
    {
      "ref": "ramgct20/attemp5",
      "title": "attemp5",
      "source": "live",
      "kernel_id": "548926"
    },
    {
      "ref": "mnoori/fancy-imputing-the-missing-inc-angles-beginner",
      "title": "Fancy imputing the missing inc_angles (beginner)",
      "source": "live",
      "kernel_id": "547794"
    },
    {
      "ref": "stefanie04736/simple-keras-model-with-k-fold-cross-validation",
      "title": "Simple Keras Model with k-fold cross validation",
      "source": "live",
      "kernel_id": "547610"
    },
    {
      "ref": "jirivrany/my-best-single-model-simple-cnn-lb-0-1541",
      "title": "My best single model - simple CNN - LB 0.1541",
      "source": "live",
      "kernel_id": "543630"
    },
    {
      "ref": "golubev/silver-best-public-kernel-0-1258-0-1374-lb",
      "title": "(??Silver)Best public kernel! (0.1258/0.1374 LB)",
      "source": "live",
      "kernel_id": "543119"
    },
    {
      "ref": "weiji14/keras-tinyyolo-based-model",
      "title": "Keras TinyYolo-based Model",
      "source": "live",
      "kernel_id": "540917"
    },
    {
      "ref": "warpri81/plotting-band-1-2-combinations",
      "title": "Plotting band 1/2 combinations",
      "source": "live",
      "kernel_id": "540433"
    },
    {
      "ref": "reppic/predicting-missing-incidence-angles",
      "title": "Predicting Missing Incidence Angles",
      "source": "live",
      "kernel_id": "539123"
    },
    {
      "ref": "poliedou/split-test-json-to-avoid-memory-issues",
      "title": "Split test.json to avoid memory issues",
      "source": "live",
      "kernel_id": "538617"
    },
    {
      "ref": "vradore/signal-extraction-and-transfer-learning",
      "title": "Signal extraction and transfer learning",
      "source": "live",
      "kernel_id": "537926"
    },
    {
      "ref": "houlong66/pytorch-test1",
      "title": "pytorch_test1",
      "source": "live",
      "kernel_id": "534921"
    },
    {
      "ref": "supersp1234/best-single-model-lb-0-1400",
      "title": "Best Single Model (LB:0.1400)",
      "source": "live",
      "kernel_id": "534606"
    },
    {
      "ref": "danieleewww/submarineering-scoring-cracking-log-loss",
      "title": "Submarineering.Scoring.Cracking Log_loss. ",
      "source": "live",
      "kernel_id": "534478"
    },
    {
      "ref": "submarineering/submarineering-scoring-cracking-log-loss",
      "title": "Submarineering.Scoring.Cracking Log_loss. ",
      "source": "live",
      "kernel_id": "532958"
    },
    {
      "ref": "n0rdic/pytorch-deepnet-definition",
      "title": "Pytorch DeepNet definition",
      "source": "live",
      "kernel_id": "530020"
    },
    {
      "ref": "hireme/two-inputs-neural-network-using-keras",
      "title": "Two-Inputs Neural Network using Keras",
      "source": "live",
      "kernel_id": "529963"
    },
    {
      "ref": "mpekalski/fft-per-band-for-normal-normalized-signal",
      "title": "FFT per band for normal/normalized signal",
      "source": "live",
      "kernel_id": "525772"
    },
    {
      "ref": "mpekalski/normalize-singal-per-inc-angle",
      "title": "Normalize singal per inc_angle",
      "source": "live",
      "kernel_id": "525752"
    },
    {
      "ref": "kevinling0218/rapid-keras-model-for-classification",
      "title": "Rapid Keras model for classification",
      "source": "live",
      "kernel_id": "524922"
    },
    {
      "ref": "warpri81/calming-the-seas-normalize-augment-bands",
      "title": "Calming the Seas - Normalize/Augment Bands",
      "source": "live",
      "kernel_id": "524511"
    },
    {
      "ref": "inoryy/api-for-keras-models-pre-trained-angles",
      "title": "API for Keras models (pre-trained, angles)",
      "source": "live",
      "kernel_id": "523942"
    },
    {
      "ref": "submarineering/submarineering-even-better-public-score-until-now",
      "title": "Submarineering.EVEN BETTER PUBLIC SCORE until now.",
      "source": "live",
      "kernel_id": "523889"
    },
    {
      "ref": "cedrickchee/boilerplate-of-keras-model-with-eda-0-210-on-lb",
      "title": "Boilerplate of Keras Model with EDA (0.210 on LB)",
      "source": "live",
      "kernel_id": "522238"
    },
    {
      "ref": "cbryant/keras-cnn-with-pseudolabeling-0-1514-lb",
      "title": "Keras CNN with Pseudolabeling (0.1514 LB)",
      "source": "live",
      "kernel_id": "519806"
    },
    {
      "ref": "warpri81/diving-for-features",
      "title": "Diving for Features",
      "source": "live",
      "kernel_id": "519588"
    },
    {
      "ref": "danieleewww/submarineering-best-public-score-until-now",
      "title": "Submarineering.BEST PUBLIC SCORE until now.",
      "source": "live",
      "kernel_id": "515006"
    },
    {
      "ref": "submarineering/submarineering-best-public-score-until-now",
      "title": "Submarineering.BEST PUBLIC SCORE until now.",
      "source": "live",
      "kernel_id": "514348"
    },
    {
      "ref": "phul65/notebook1",
      "title": "Notebook1",
      "source": "live",
      "kernel_id": "514201"
    },
    {
      "ref": "hosseinnamazian/simple-cnn-model-in-tensorflow",
      "title": "Simple CNN model in tensorflow",
      "source": "live",
      "kernel_id": "511097"
    },
    {
      "ref": "takuok/keras-smallcnn-and-vgg16-fine-tuning",
      "title": "Keras smallCNN and VGG16 fine tuning",
      "source": "live",
      "kernel_id": "508683"
    },
    {
      "ref": "bowyee/explore-stacking-lb-0-1463",
      "title": "Explore Stacking (LB 0.1463)",
      "source": "live",
      "kernel_id": "507343"
    },
    {
      "ref": "yikoudreams/feature-extraction-by-resnet-cv-added",
      "title": "Feature Extraction by ResNet (cv added)",
      "source": "live",
      "kernel_id": "505778"
    },
    {
      "ref": "chechir/explore-stacking-another-hi-lo-and-clip-probs",
      "title": "Explore Stacking another hi-lo and clip probs",
      "source": "live",
      "kernel_id": "504080"
    },
    {
      "ref": "uchihasean/keras-cnn-band-inc-angle",
      "title": "Keras CNN  (band + inc_angle)",
      "source": "live",
      "kernel_id": "503140"
    },
    {
      "ref": "arpandhatt/package-data",
      "title": "Package Data",
      "source": "live",
      "kernel_id": "501451"
    },
    {
      "ref": "submarineering/submarineering-what-about-volume",
      "title": "Submarineering. What about Volume.",
      "source": "live",
      "kernel_id": "500986"
    },
    {
      "ref": "volody/logistic-regression",
      "title": "Logistic Regression",
      "source": "live",
      "kernel_id": "500109"
    },
    {
      "ref": "ayanmaity/fork-of-iceberg-recognition",
      "title": "Fork of Iceberg Recognition",
      "source": "live",
      "kernel_id": "498280"
    },
    {
      "ref": "rbhambri/run3-with-df-pickled",
      "title": "run3 with df pickled",
      "source": "live",
      "kernel_id": "497773"
    },
    {
      "ref": "henrywolf/simple-convnet-in-keras",
      "title": "simple convnet in keras",
      "source": "live",
      "kernel_id": "497589"
    },
    {
      "ref": "ayanmaity/iceberg-recognition-using-keras",
      "title": "Iceberg Recognition",
      "source": "live",
      "kernel_id": "497305"
    },
    {
      "ref": "skgunnala/keras-resnet-with-image-augmentation",
      "title": "Keras ResNet with image augmentation",
      "source": "live",
      "kernel_id": "495987"
    },
    {
      "ref": "treina/keras-resnet-with-image-augmentation",
      "title": "Keras ResNet with image augmentation",
      "source": "live",
      "kernel_id": "493194"
    },
    {
      "ref": "alekseynp/on-careful-validation-in-your-keras-kernels",
      "title": "On Careful Validation in your Keras Kernels",
      "source": "live",
      "kernel_id": "491545"
    },
    {
      "ref": "aevinq/cnn-batchnormalization-0-1646",
      "title": "CNN+ BatchNormalization,0.1646",
      "source": "live",
      "kernel_id": "490030"
    },
    {
      "ref": "submarineering/submarineering-objects-isolation-0-75-lb",
      "title": "Submarineering. Objects isolation.( <0.75 LB)",
      "source": "live",
      "kernel_id": "489963"
    },
    {
      "ref": "thariqkhalid/simple-eda-image-formation-with-numpy",
      "title": "Simple EDA & Image formation with numpy",
      "source": "live",
      "kernel_id": "488563"
    },
    {
      "ref": "rbhambri/keras-model-for-beginners-0-210-on-lb-eda-r-d",
      "title": "Keras Model for Beginners (0.210 on LB)+EDA+R&D",
      "source": "live",
      "kernel_id": "487942"
    },
    {
      "ref": "henokanh/cnn-batchnormalization-0-1646",
      "title": "CNN+ BatchNormalization,0.1646",
      "source": "live",
      "kernel_id": "486354"
    },
    {
      "ref": "glowingbazooka/semi-supervised-model-using-keras",
      "title": "Semi supervised Model-Using Keras",
      "source": "live",
      "kernel_id": "485514"
    },
    {
      "ref": "fabsta/getvgganglemodel-mobilenet-lb-0-1626",
      "title": "getVggAngleModel + MobileNet (LB 0.1626)",
      "source": "live",
      "kernel_id": "482524"
    },
    {
      "ref": "yuhaichina/single-model-vgg16-mobilenet-lb-0-1568-with-tf",
      "title": "Single Model (VGG16+MobileNet) LB:0.1568 with tf",
      "source": "live",
      "kernel_id": "480766"
    },
    {
      "ref": "praveenjune17/sigmoid-model",
      "title": "Sigmoid model",
      "source": "live",
      "kernel_id": "478711"
    },
    {
      "ref": "jgroff/despeckling-synthetic-aperture-radar-sar-images",
      "title": "Despeckling Synthetic Aperture Radar (SAR) Images ",
      "source": "live",
      "kernel_id": "476190"
    },
    {
      "ref": "bekaaa/applying-tensorflow",
      "title": "applying tensorflow",
      "source": "live",
      "kernel_id": "474575"
    },
    {
      "ref": "tienanh2007/mu-fem-tienanhnguyen",
      "title": "MU-FEM-TIENANHNGUYEN",
      "source": "live",
      "kernel_id": "472408"
    },
    {
      "ref": "hcc1995/keras-cnn-model",
      "title": "Keras' CNN Model",
      "source": "live",
      "kernel_id": "469774"
    },
    {
      "ref": "a45632/keras-starter-4l-added-performance-graph",
      "title": "Keras starter 4L - added performance graph",
      "source": "live",
      "kernel_id": "468421"
    },
    {
      "ref": "avarian/tugas-dmkb",
      "title": "Tugas DMKB",
      "source": "live",
      "kernel_id": "465249"
    },
    {
      "ref": "vincento/keras-starter-4l-0-1694-lb-icebergchallenge",
      "title": "Keras starter 4L - 0.1694 LB IcebergChallenge",
      "source": "live",
      "kernel_id": "462158"
    },
    {
      "ref": "bphlmn/what-can-we-do-with-logistic-regression",
      "title": "What can we do with Logistic Regression?",
      "source": "live",
      "kernel_id": "461715"
    },
    {
      "ref": "yanchao727/pretrained-inception-v4-0-2-pl",
      "title": "pretrained inception v4 ~0.2 PL",
      "source": "live",
      "kernel_id": "460542"
    },
    {
      "ref": "dongxu027/explore-stacking-lb-0-1463",
      "title": "Explore Stacking (LB 0.1463)",
      "source": "live",
      "kernel_id": "456536"
    },
    {
      "ref": "atom1231/keras-autoencoder-with-simple-cnn-kfold4-lb-1704",
      "title": "Keras AutoEncoder with simple CNN(kfold4/LB .1704)",
      "source": "live",
      "kernel_id": "456302"
    },
    {
      "ref": "submarineering/submarineering-size-matters-0-75-lb",
      "title": "Submarineering. Size Matters( <0.75 LB)",
      "source": "live",
      "kernel_id": "455319"
    },
    {
      "ref": "shivml89/iceberg-dataset-analysis-using-python",
      "title": "Iceberg Dataset Analysis using Python",
      "source": "live",
      "kernel_id": "454126"
    },
    {
      "ref": "fvzaur/iceberg-ship-classification-with-cnn-on-keras",
      "title": "Iceberg/Ship classification with CNN on Keras",
      "source": "live",
      "kernel_id": "453818"
    },
    {
      "ref": "cbryant/keras-cnn-statoil-iceberg-lb-0-1995-now-0-1516",
      "title": "Keras CNN - StatOil Iceberg LB 0.1995 (now 0.1516)",
      "source": "live",
      "kernel_id": "450863"
    },
    {
      "ref": "shravyam/notebook395e095bb9",
      "title": "Notebook395e095bb9",
      "source": "live",
      "kernel_id": "447424"
    },
    {
      "ref": "yekenot/inceptionv3-k-fold-cv-lb-0-1944",
      "title": "InceptionV3 K-fold CV (LB 0.1944)",
      "source": "live",
      "kernel_id": "446700"
    },
    {
      "ref": "danieleewww/statoil-csv-pytorch-densenet-ensemble-lb-0-1610",
      "title": "Statoil CSV PyTorch DenseNet ensemble LB 0.1610",
      "source": "live",
      "kernel_id": "446560"
    },
    {
      "ref": "surajdeshmukh/keras-batchnorm-0-19",
      "title": "keras+batchnorm+0.19",
      "source": "live",
      "kernel_id": "446414"
    },
    {
      "ref": "tejasshahpuri/keras-plus-tensorflow",
      "title": "Keras plus TensorFlow",
      "source": "live",
      "kernel_id": "445782"
    },
    {
      "ref": "solomonk/statoil-csv-pytorch-senet-ensemble-lb-0-1520",
      "title": "Statoil CSV PyTorch SENet ensemble LB 0.1520",
      "source": "live",
      "kernel_id": "443698"
    },
    {
      "ref": "danieleewww/keras-tf-lb",
      "title": "Keras+TF LB",
      "source": "live",
      "kernel_id": "443116"
    },
    {
      "ref": "n3n77i/statoil-jlv1-80",
      "title": "statoil.jlv1.80",
      "source": "live",
      "kernel_id": "443088"
    },
    {
      "ref": "wvadim/keras-tf-lb-0-18",
      "title": "Keras+TF LB 0.18",
      "source": "live",
      "kernel_id": "442846"
    },
    {
      "ref": "sudosudoohio/xgb-transfer-learning-lb-0-3040",
      "title": "XGB + Transfer Learning (LB 0.3040)",
      "source": "live",
      "kernel_id": "440273"
    },
    {
      "ref": "dimitrif/y-augmentation-coordonate-with-tsne",
      "title": "Y augmentation: Coordonate (with tSNE)",
      "source": "live",
      "kernel_id": "439952"
    },
    {
      "ref": "towever/transfer-learning-with-vgg-16-convnet-lb-0-1850",
      "title": "Transfer Learning with VGG-16 ConvNet LB 0.1850",
      "source": "live",
      "kernel_id": "438924"
    },
    {
      "ref": "n3n77i/statoil-jlv0",
      "title": "statoil.jlv0",
      "source": "live",
      "kernel_id": "438872"
    },
    {
      "ref": "sdalvi/pytorch-cnn-sixth-generation",
      "title": "PyTorch CNN sixth generation ",
      "source": "live",
      "kernel_id": "437664"
    },
    {
      "ref": "yuqingxue/a-keras-prototype-0-21174-on-pl",
      "title": "A keras prototype (0.21174 on PL)",
      "source": "live",
      "kernel_id": "436890"
    },
    {
      "ref": "camnugent/convolutional-neural-net-basic-tensorflow",
      "title": "Convolutional Neural Net - Basic TensorFlow",
      "source": "live",
      "kernel_id": "436471"
    },
    {
      "ref": "ejackf/simple-example",
      "title": "Simple example",
      "source": "live",
      "kernel_id": "435977"
    },
    {
      "ref": "jasonbenner/shallownet-with-features",
      "title": "Shallownet with features",
      "source": "live",
      "kernel_id": "435234"
    },
    {
      "ref": "sudosudoohio/use-pretrained-model-in-keras-with-statoil-dataset",
      "title": "Use Pretrained Model in Keras with Statoil dataset",
      "source": "live",
      "kernel_id": "434939"
    },
    {
      "ref": "solomonk/basic-frequency-domain-fft-top-psd-32-peaks",
      "title": "Basic frequency domain: FFT top PSD 32 peaks",
      "source": "live",
      "kernel_id": "434625"
    },
    {
      "ref": "timolee/fastai-kaggle-starter-kit-lb-0-33",
      "title": "FastAI Kaggle Starter Kit (LB 0.33)",
      "source": "live",
      "kernel_id": "434101"
    },
    {
      "ref": "bluevalhalla/fully-convolutional-network-lb-0-193",
      "title": "Fully Convolutional Network (LB 0.193)",
      "source": "live",
      "kernel_id": "434083"
    },
    {
      "ref": "grroverpr/eda-and-cnn-resnet-18-lb-0-2094",
      "title": "EDA and CNN (resnet-18) (LB 0.2094)",
      "source": "live",
      "kernel_id": "433843"
    },
    {
      "ref": "darrellulm/keras-test-run",
      "title": "keras Test Run",
      "source": "live",
      "kernel_id": "432793"
    },
    {
      "ref": "loveall/mr-freeze-s-ocean-7498fd",
      "title": "Mr Freeze's Ocean 7498fd",
      "source": "live",
      "kernel_id": "432609"
    },
    {
      "ref": "loveall/pytorch-gpu-cnn-bceloss-predictions-gokagglers",
      "title": "PyTorch GPU CNN & BCELoss predictions_GoKagglers",
      "source": "live",
      "kernel_id": "431980"
    },
    {
      "ref": "devm2024/transfer-learning-with-vgg-16-cnn-aug-lb-0-1712",
      "title": "Transfer Learning with VGG-16 CNN+AUG LB 0.1712",
      "source": "live",
      "kernel_id": "431763"
    },
    {
      "ref": "loveall/mr-freeze-s-ocean-gk",
      "title": "Mr Freeze's Ocean_GK",
      "source": "live",
      "kernel_id": "431683"
    },
    {
      "ref": "sjoly123/ensembling-gbms-lb-203",
      "title": "Ensembling GBMs (LB .203~",
      "source": "live",
      "kernel_id": "431583"
    },
    {
      "ref": "jasonbenner/lets-try-capsulenet",
      "title": "Lets try CapsuleNet",
      "source": "live",
      "kernel_id": "428968"
    },
    {
      "ref": "rbhambri/trial1",
      "title": "trial1",
      "source": "live",
      "kernel_id": "428406"
    },
    {
      "ref": "strali/keras-resnet-with-image-augmentation",
      "title": "Keras ResNet with image augmentation",
      "source": "live",
      "kernel_id": "428350"
    },
    {
      "ref": "samuelsujith/lee-filter",
      "title": "lee filter",
      "source": "live",
      "kernel_id": "427859"
    },
    {
      "ref": "samuelsujith/integrate-angle-into-calculations",
      "title": "Integrate angle into calculations",
      "source": "live",
      "kernel_id": "426255"
    },
    {
      "ref": "loveall/keras-model-for-beginners-0-210-on-lb-eda-r-d",
      "title": "Keras Model for Beginners (0.210 on LB)+EDA+R&D",
      "source": "live",
      "kernel_id": "424779"
    },
    {
      "ref": "dimitrif/y-augmentation-object-sizes",
      "title": "Y augmentation: Object sizes",
      "source": "live",
      "kernel_id": "423891"
    },
    {
      "ref": "devm2024/keras-model-for-beginners-0-210-on-lb-eda-r-d",
      "title": "Keras Model for Beginners (0.210 on LB)+EDA+R&D",
      "source": "live",
      "kernel_id": "423371"
    },
    {
      "ref": "vinkaga/a-keras-prototype-0-21174-with-visualizations",
      "title": "A keras prototype (0.21174) with Visualizations",
      "source": "live",
      "kernel_id": "423265"
    },
    {
      "ref": "jasonbenner/basic-resnet50-with-composites-0-2502lb",
      "title": "Basic ResNet50 with composites 0.2502LB",
      "source": "live",
      "kernel_id": "422858"
    },
    {
      "ref": "plarmuseau/svd-solver",
      "title": "svd solver",
      "source": "live",
      "kernel_id": "422438"
    },
    {
      "ref": "green05031/first-image-recognition",
      "title": "first image recognition",
      "source": "live",
      "kernel_id": "421258"
    },
    {
      "ref": "rajsaladi/svm-hog-on-colourcompositeimage",
      "title": "SVM+HOG On ColourCompositeImage",
      "source": "live",
      "kernel_id": "420133"
    },
    {
      "ref": "supersp1234/tools-for-pytorch-transform",
      "title": "tools for pytorch transform",
      "source": "live",
      "kernel_id": "419541"
    },
    {
      "ref": "keremt/getting-color-composites",
      "title": "GETTING COLOR COMPOSITES",
      "source": "live",
      "kernel_id": "419390"
    },
    {
      "ref": "samuelsujith/image-generator-augmentation-and-convnet",
      "title": "Image Generator Augmentation and ConvNet",
      "source": "live",
      "kernel_id": "419369"
    },
    {
      "ref": "jackvial/a-brief-intro-to-convolution",
      "title": "A Brief Intro to Convolution",
      "source": "live",
      "kernel_id": "418674"
    },
    {
      "ref": "heyt0ny/pytorch-custom-dataload-with-augmentaion",
      "title": "Pytorch custom Dataload with augmentaion",
      "source": "live",
      "kernel_id": "418407"
    },
    {
      "ref": "solomonk/pytorch-modular-and-clean-cnn",
      "title": "PyTorch Modular and clean CNN",
      "source": "live",
      "kernel_id": "418300"
    },
    {
      "ref": "ezietsman/simple-keras-convnet",
      "title": "Simple Keras Convnet.",
      "source": "live",
      "kernel_id": "417591"
    },
    {
      "ref": "dimitrif/feature-engineering-wavelet-transformation",
      "title": "Feature engineering: Wavelet transformation",
      "source": "live",
      "kernel_id": "416302"
    },
    {
      "ref": "dimitrif/other-sentinel-data",
      "title": "Other sentinel data",
      "source": "live",
      "kernel_id": "415766"
    },
    {
      "ref": "tomwarrens/first-model-in-tensorflow",
      "title": "First Model in Tensorflow",
      "source": "live",
      "kernel_id": "415015"
    },
    {
      "ref": "huseinzol05/2d-3d-visualization-using-nce-cost",
      "title": "2D & 3D Visualization using NCE Cost",
      "source": "live",
      "kernel_id": "414987"
    },
    {
      "ref": "solomonk/pytorch-gpu-cnn-bceloss-with-predictions",
      "title": "PyTorch GPU CNN & BCELoss with predictions",
      "source": "live",
      "kernel_id": "413831"
    },
    {
      "ref": "solomonk/pytorch-gpu-based-cnn-bceloss-with-predictions",
      "title": "PyTorch GPU based CNN & BCELoss with predictions",
      "source": "live",
      "kernel_id": "413127"
    },
    {
      "ref": "solomonk/pytorch-densenet-angle-rgb-dataloader",
      "title": "Pytorch DenseNet + Angle + RGB DataLoader",
      "source": "live",
      "kernel_id": "413103"
    },
    {
      "ref": "loveall/domain-knowledge",
      "title": "Domain knowledge",
      "source": "live",
      "kernel_id": "412676"
    },
    {
      "ref": "loveall/keras-with-data-augmentation-lb-0-1826",
      "title": "keras with data augmentation (LB: 0.1826)",
      "source": "live",
      "kernel_id": "411564"
    },
    {
      "ref": "keithwm/data-exploration",
      "title": "Data exploration",
      "source": "live",
      "kernel_id": "411505"
    },
    {
      "ref": "plarmuseau/fork-of-fork-of-myopic-research",
      "title": "Fork of Fork of Myopic research ",
      "source": "live",
      "kernel_id": "411049"
    },
    {
      "ref": "dimitrif/domain-knowledge",
      "title": "Domain knowledge",
      "source": "live",
      "kernel_id": "410556"
    },
    {
      "ref": "loveall/keras-with-data-augmentation-6678d5",
      "title": "keras with data augmentation 6678d5",
      "source": "live",
      "kernel_id": "410279"
    },
    {
      "ref": "huseinzol05/self-optimized-cnn-using-bayesian",
      "title": "Self Optimized CNN using Bayesian",
      "source": "live",
      "kernel_id": "410158"
    },
    {
      "ref": "weizhezhang/a-gluon-starter",
      "title": "A gluon starter",
      "source": "live",
      "kernel_id": "409727"
    },
    {
      "ref": "dimitrif/keras-with-data-augmentation-lb-0-1826",
      "title": "keras with data augmentation (LB: 0.1826)",
      "source": "live",
      "kernel_id": "409333"
    },
    {
      "ref": "plarmuseau/simple-svd-xgboost-baseline-lb-35-5113d7",
      "title": "Simple SVD + xgboost baseline (LB .35) 5113d7",
      "source": "live",
      "kernel_id": "409326"
    },
    {
      "ref": "plarmuseau/myopic-research",
      "title": "Myopic research ",
      "source": "live",
      "kernel_id": "408049"
    },
    {
      "ref": "cttsai/feature-extraction-by-resnet-keras",
      "title": "Feature Extraction by ResNet (keras ",
      "source": "live",
      "kernel_id": "408003"
    },
    {
      "ref": "glebmihaescu/logistic-regression-0-44lb",
      "title": "Logistic Regression (0.44LB)",
      "source": "live",
      "kernel_id": "407763"
    },
    {
      "ref": "cttsai/ensembling-gbms-lb-203",
      "title": "Ensembling GBMs (LB .203~",
      "source": "live",
      "kernel_id": "407636"
    },
    {
      "ref": "plarmuseau/how-to-use-the-angle-imho",
      "title": "how to use the angle: IMHO",
      "source": "live",
      "kernel_id": "407063"
    },
    {
      "ref": "asindico/icebergs-and-ships-eda-and-augmentation",
      "title": "Icebergs and Ships EDA and Augmentation",
      "source": "live",
      "kernel_id": "407028"
    },
    {
      "ref": "muonneutrino/exploration-transforming-images-in-python",
      "title": "Exploration & Transforming Images in Python",
      "source": "live",
      "kernel_id": "407003"
    },
    {
      "ref": "camnugent/expanded-training-set-keras-imagedatagenerator",
      "title": "Expanded training set: keras ImageDataGenerator",
      "source": "live",
      "kernel_id": "406712"
    },
    {
      "ref": "sinkie/keras-data-augmentation-with-multiple-inputs",
      "title": "Keras data augmentation with multiple inputs",
      "source": "live",
      "kernel_id": "406564"
    },
    {
      "ref": "ivalmian/simple-svd-xgboost-baseline-lb-35",
      "title": "Simple SVD + xgboost baseline (LB .35)",
      "source": "live",
      "kernel_id": "406404"
    },
    {
      "ref": "skhemka/keras-modified-0-29-on-pl",
      "title": "Keras -modified (~ 0.29 on PL)",
      "source": "live",
      "kernel_id": "406270"
    },
    {
      "ref": "danofer/keras-straightforward-fork",
      "title": "Keras - straightforward fork",
      "source": "live",
      "kernel_id": "405775"
    },
    {
      "ref": "knowledgegrappler/a-keras-prototype-0-21174-on-pl",
      "title": "A keras prototype (0.21174 on PL)",
      "source": "live",
      "kernel_id": "405532"
    },
    {
      "ref": "loveall/will-it-sink",
      "title": "Will it sink?",
      "source": "live",
      "kernel_id": "405467"
    },
    {
      "ref": "brassmonkey381/viewing-leak-and-machine-images",
      "title": "Viewing Leak and Machine Images",
      "source": "live",
      "kernel_id": "405330"
    },
    {
      "ref": "nanigans/pytorch-starter",
      "title": "Pytorch starter",
      "source": "live",
      "kernel_id": "405297"
    },
    {
      "ref": "brassmonkey381/exploring-leaky-incidence-angle",
      "title": "Exploring Leaky Incidence Angle",
      "source": "live",
      "kernel_id": "405254"
    },
    {
      "ref": "mihaskalic/keras-straightforward",
      "title": "Keras - straightforward",
      "source": "live",
      "kernel_id": "404935"
    },
    {
      "ref": "kmader/exploring-the-icebergs-with-skimage-and-keras",
      "title": "Exploring the Icebergs with skimage and Keras",
      "source": "live",
      "kernel_id": "404932"
    },
    {
      "ref": "mihaskalic/will-it-sink",
      "title": "Will it sink?",
      "source": "live",
      "kernel_id": "404906"
    },
    {
      "ref": "loveall/plotting-icebergs-and-boats",
      "title": "Plotting Icebergs and Boats",
      "source": "live",
      "kernel_id": "404893"
    },
    {
      "ref": "juanumusic/plotting-icebergs-and-boats",
      "title": "Plotting Icebergs and Boats",
      "source": "live",
      "kernel_id": "404828"
    },
    {
      "ref": "itratrahman/convolutional-neural-net-tutorial-tensorflow",
      "title": "Convolutional Neural Net Tutorial (tensorflow) ",
      "source": "live",
      "kernel_id": "400812"
    },
    {
      "ref": "preeth/bike-share-analysis",
      "title": "",
      "source": "meta",
      "kernel_id": "447705"
    },
    {
      "ref": "matteocasadei/icebergs-a-stacking-based-approach",
      "title": "",
      "source": "meta",
      "kernel_id": "527265"
    },
    {
      "ref": "aloisiodn/weighted-lstm-nb-svm-cnn-blend-lb-0-044",
      "title": "",
      "source": "meta",
      "kernel_id": "511624"
    },
    {
      "ref": "friedda1/iceberg-wvadim-credit-py",
      "title": "",
      "source": "meta",
      "kernel_id": "544608"
    },
    {
      "ref": "arpandhatt/siamese-neural-networks",
      "title": "",
      "source": "meta",
      "kernel_id": "743610"
    }
  ],
  "errors": []
}