【文件属性】：
文件名称：NLP:NLP，韩文，Konlpy，文本分类
文件大小：17.86MB
文件格式：ZIP
更新时间：2021-03-19 16:41:28
JupyterNotebook
NLP:NLP，韩文，Konlpy，文本分类
【文件预览】：
NLP-master
----Seq2Seq+Attention_mechanism_for_reciept_OCR_error_fix2.ipynb(130KB)
----2020NLU의 근황에 대한 나의 생각.txt(4KB)
----Soynlp_단어추출,_토큰화.ipynb(37KB)
----Detectron2_semi_haram03_17.ipynb(18.23MB)
----Keras_Word_Embedding.ipynb(1.15MB)
----receipt_transformer_pytorch_kobert.ipynb(32KB)
----Google_SentencePiece_단어_뭉치_만들기.ipynb(12KB)
----seq2seq_만들기(Simple_seq2seq).ipynb(4KB)
----KoBERT.ipynb(5KB)
----seq2seq_translation_tutorial.ipynb(50KB)
----영수증_seq2seq_attention(ver_2).ipynb(338KB)
----HSV_색상_분리하기.ipynb(69KB)
----딥러닝_모델_평가에_대해서.ipynb(8.49MB)
----영수증_맞춤법_seq2seq로_해결하기(receipt_seq2seq)의_수정본.ipynb(81KB)
----Transformer_(Attention_Is_All_You_Need)_구현하기.ipynb(48KB)
----pyLDAvis_를_이용한_Latent_Dirichlet_Allocation_시각화.ipynb(46KB)
----자연어_처리_텐서플로우로_텍스트_분류_튜토리얼(2).ipynb(50KB)
----(NLP)Transformer.ipynb(42KB)
----BERT_End_to_End_(Fine_tuning_+_Predicting)_with_Cloud_TPU_Sentence_and_Sentence_Pair_Classification_Tasks.ipynb(614KB)
----BERT(Bidirectional_Encoder_Representations_from_Transformers)_구현(기준).ipynb(69KB)
----Spellcheck.ipynb(6KB)
----Transformer용_텍스트_데이터_전처리하기.ipynb(6KB)
----py_hanspell.ipynb(9KB)
----PyTorch_및_TensorFlow_2_0을위한_최신_자연_언어_처리.ipynb(3KB)
----Word2Vec_Embedding.ipynb(35KB)
----Pre_trained_Word_Embedding_03_16.ipynb(1005KB)
----AutoCorrect_Spell_check_in_Python.ipynb(11KB)
----Transformer_(Attention_Is_All_You_Need)_구현하기의_사본.ipynb(50KB)
----Document_Similarity_Jaccard_similarity.ipynb(41KB)
----naver_review_classifications_pytorch_kobert_ipynb의_사본.ipynb(13KB)
----간단한_seq2seq_만들기(Simple_seq2seq).ipynb(49KB)
----bert_babble.ipynb(25KB)
----자연어_처리_텐서플로우로_텍스트_분류_튜토리얼.ipynb(55KB)
----Pre_trained_Word_Embedding_GloVe.ipynb(1.65MB)
----Transformer_임시 수정본.ipynb(49KB)
----영수증_맞춤법_seq2seq로_해결하기(receipt_seq2seq).ipynb(208KB)
----레벤슈타인_거리.ipynb(9KB)
----자연어_처리_케라스로_텍스트_분류.ipynb(1.56MB)
----GloVe_Embedding.ipynb(43KB)
----Keras_Word_Embedding(14labels).ipynb(2.07MB)