Machine_Learning

1.Python Basics with Numpy

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2.Logistic Regression with a Neural Network mindset

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3.Planar data classification with one hidden layer

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4.Building Deep Neural Network : step_by_step.ver

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5.Deep Neural Network-Application

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6.순환 신경망 Recurrent Neural Network(RNN)

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7.Initialization (with blue/red dots in circles dataset)

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8.RNN Language Model (char 단위)

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9.Regularization(L2 Regularization, Dropout)

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10.Gradient Checking

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11.Simple RNN/LSTM 이해하기

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12.Reuters News Classification (로이터 뉴스 분류하기)

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13.IMDB Classification (IMDB 이진 분류)

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14.ELMo

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15.Optimization Methods

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16.Multi-Kernel 1D CNN

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17.Intent Classification

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18.Intent Classification

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19.POS Tagging with Bidirectional LSTM

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20.Introduction to Tensorflow

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21.Named Entity Recognition 개체명 인식

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22.Named Entity Recognition with BiLSTM

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23.Named Entity Recognition with BiLSTM + CNN

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24.Building RNN - step by step

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