data/skorch-0.9.0/docs/user/FAQ.rst:62: coresponding ==> corresponding
data/skorch-0.9.0/docs/user/REST.rst:5: classifer ==> classifier
data/skorch-0.9.0/docs/user/callbacks.rst:38: overriden ==> overridden
data/skorch-0.9.0/docs/user/callbacks.rst:96: ususal ==> usual
data/skorch-0.9.0/docs/user/neuralnet.rst:57: Thefore ==> Therefore
data/skorch-0.9.0/docs/user/neuralnet.rst:234: additonal ==> additional
data/skorch-0.9.0/docs/user/neuralnet.rst:419: ouputs ==> outputs
data/skorch-0.9.0/docs/user/neuralnet.rst:528: Additonally ==> Additionally
data/skorch-0.9.0/docs/user/parallelism.rst:22: distrubted ==> distributed, disrupted
data/skorch-0.9.0/examples/benchmarks/mnist.py:4: eachother ==> each other
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:14: offical ==> official
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: UE ==> USE, DUE
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: Fo ==> Of, for
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: bA ==> by, be
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: te ==> the, be, we
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: PASe ==> pass, pace, parse
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:86: fOf ==> for
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:493: tE ==> the, be, we
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:493: te ==> the, be, we
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:493: wEe ==> we
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:493: tE ==> the, be, we
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:493: Bve ==> Be
data/skorch-0.9.0/examples/nuclei_image_segmentation/Nuclei_Image_Segmentation.ipynb:522: Whats ==> What's
data/skorch-0.9.0/examples/nuclei_image_segmentation/prepare_dataset.py:2: theese ==> these
data/skorch-0.9.0/examples/rnn_classifer/RNN_sentiment_classification.ipynb:334: heterogenous ==> heterogeneous
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:524: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:539: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:2364: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:2366: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:2367: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:2369: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:2559: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/test.txt:3496: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:76: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:3698: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:3756: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5540: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5787: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5821: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5824: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5827: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5880: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5884: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5886: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:5933: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:6793: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:6943: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:7140: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:7239: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:8852: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:10402: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:10842: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:14782: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16156: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16358: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16453: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16454: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16455: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16460: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16461: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16462: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16463: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16464: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16465: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16467: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16469: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16470: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16471: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16474: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16477: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16478: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:16810: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:17558: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:19069: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:19079: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:19292: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:19324: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:21217: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:21584: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:22954: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23345: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23540: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23542: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23811: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23842: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:23845: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:24203: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:24205: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:24207: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:24238: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:24243: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:25972: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:26488: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:26550: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:28493: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:28707: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:28708: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:29333: pont ==> point
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:30007: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:30965: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31567: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31567: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31569: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31576: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31580: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31587: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:31588: thi ==> the, this
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33225: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33402: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33406: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33411: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33938: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:33974: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34180: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34184: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34185: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34186: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34192: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34194: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34196: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34199: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34200: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34201: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34201: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34202: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34205: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34213: rouge ==> rogue
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34388: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34389: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34390: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34391: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34392: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34392: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:34393: doman ==> domain
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:36424: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37184: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37354: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37356: hart ==> heart, harm
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37761: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37772: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37826: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37831: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:37837: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:38597: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:38608: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:38722: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:38740: bloc ==> block
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:39372: trough ==> through
data/skorch-0.9.0/examples/word_language_model/data/penn/train.txt:41264: bloc ==> block
data/skorch-0.9.0/notebooks/Advanced_Usage.ipynb:726: implicitely ==> implicitly
data/skorch-0.9.0/notebooks/Advanced_Usage.ipynb:1532: outpus ==> output, outputs
data/skorch-0.9.0/notebooks/MNIST-torchvision.ipynb:456: UE ==> USE, DUE
data/skorch-0.9.0/notebooks/MNIST-torchvision.ipynb:501: formated ==> formatted
data/skorch-0.9.0/notebooks/MNIST.ipynb:218: 1nd ==> 1st
data/skorch-0.9.0/skorch/cli.py:255: defautls ==> defaults
data/skorch-0.9.0/skorch/cli.py:302: defautls ==> defaults
data/skorch-0.9.0/skorch/dataset.py:182: conjuction ==> conjunction
data/skorch-0.9.0/skorch/helper.py:210: conjuction ==> conjunction
data/skorch-0.9.0/skorch/net.py:135: initialzed ==> initialized
data/skorch-0.9.0/skorch/net.py:479: compoment ==> component
data/skorch-0.9.0/skorch/net.py:1644: initilisation ==> initialisation, initialization
data/skorch-0.9.0/skorch/utils.py:75: COO ==> COUP
data/skorch-0.9.0/skorch/callbacks/logging.py:281: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:282: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:283: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:287: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:308: occuring ==> occurring
data/skorch-0.9.0/skorch/callbacks/logging.py:704: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:705: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:709: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:721: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:722: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/logging.py:724: hist ==> heist, his
data/skorch-0.9.0/skorch/callbacks/scoring.py:42: uesd ==> used
data/skorch-0.9.0/skorch/callbacks/scoring.py:204: recommnded ==> recommended
data/skorch-0.9.0/skorch/tests/test_cli.py:23: libarary ==> library
data/skorch-0.9.0/skorch/tests/test_dataset.py:560: calld ==> called
data/skorch-0.9.0/skorch/tests/test_helper.py:384: directy ==> directly
data/skorch-0.9.0/skorch/tests/test_net.py:261: erronously ==> erroneously
data/skorch-0.9.0/.pc/skip-test.patch/skorch/tests/test_net.py:261: erronously ==> erroneously