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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25.Faster R-CNN Background

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26.Faster R-CNN Method and Results

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27.DL Day1

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28.DL Day2

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29.Backpropagation 코드실습

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30.인공 뉴런 개요

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31.DL Day4

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32.Binary Cross Entropy 도함수 구하기

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33.Backpropagation

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34.Artificial Neuron

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35.Hyperplane and Decision Boundaries of Hyperplane

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36.실습 with MNIST Dataset

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37.Cross Entropy 실습

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38.Softmax

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39.Neural Network 구현하기

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40.Multilayer Perceptron 학습시키기

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41.MNIST on MLP 실습

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42.Convolutional Layer

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43.Pooling Layer

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44.LeNet5 구현하기

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