{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"markdown","source":"# NLP Challange.\n\nI have decided a idealistic challange for myself that is learning NLP in 1 week \nthen participating in  \"Jigsaw Multilingual Toxic comment classification challange\"\n\nI am going to write all the things I will learn in different notebooks.\n\n\nThis the notebook where i apply some things I learned in my 1 week challange\n[Jigsaw - tensorflow hub vs Hugging face](https://www.kaggle.com/maunish/jigsaw-tensorflow-hub-vs-hugging-face)\n\n\n\nBelow are the  notebooks which covers my whole 1 week journey.\n\n**NOTE**: Notebooks provided below are not form this compitition, they are from [\"spooky author identification\"](https://www.kaggle.com/c/spooky-author-identification). as I wanted to start with easy data and then take part in.<br/>\nJigsaw challange.\n\n\n[Notebook1](https://www.kaggle.com/maunish/nlp-challenge-part-1)<br/>\nIn first notebook i cover topics like.\n1. what is NLP?\n2. Tf (term frequency) and idf (inverse document frequency)\n3. CountVectorizer\n4. TfidfVectorizer and TfidfTransformer\n5. Training Logistic Regression, SVM, XGboost, Navie Bayes,\n6. GridSearch.\n\n[Notebook2](https://www.kaggle.com/maunish/nlp-challenge-part-2)\ncontent of second notebook.\n\n1. What is Word vectors and word embeddings?\n2. Simple NN\n3. RNN\n4. LSTM\n5. GRU\n6. Bidirectional LSTM\n\n[Notebook3](https://www.kaggle.com/maunish/nlp-challange-part3)\ncontents of third notebook.\n\n1. Attention models\n2. What is transformer\n3. BERT.\n\n\nThis all notebook is from the \"spooky author identification\"<br/>\nI will write notebook on \"Jigsaw Multilingual Toxic Comment Classification\" soon."},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}