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Dialogue: 0,0:00:09.00,0:00:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:00:10.00,0:00:13.00,Default,,0000,0000,0000,,Esta apresentação é disponibilizada pelo centro Standford para desenvolvimento
Dialogue: 0,0:00:13.00,0:00:20.00,Default,,0000,0000,0000,,profissional
Dialogue: 0,0:00:23.00,0:00:26.00,Default,,0000,0000,0000,,Ok. Bom dia e bem vindos de volta
Dialogue: 0,0:00:26.00,0:00:30.00,Default,,0000,0000,0000,,a terceira palestra para esta classe. Então,
Dialogue: 0,0:00:30.00,0:00:33.00,Default,,0000,0000,0000,,aqui está o que quero fazer hoje
Dialogue: 0,0:00:33.00,0:00:34.00,Default,,0000,0000,0000,,E... em alguns dos
Dialogue: 0,0:00:34.00,0:00:37.00,Default,,0000,0000,0000,,tópicos que eu irei fazer hoje podem parecer um pouco que eu esteja meio que, pulando
Dialogue: 0,0:00:37.00,0:00:41.00,Default,,0000,0000,0000,,de um tópico a outro, mas aqui está, mais ou menos, o roteiro para hoje e o
Dialogue: 0,0:00:41.00,0:00:44.00,Default,,0000,0000,0000,,fluxo lógico de idéias.
Dialogue: 0,0:00:44.00,0:00:48.00,Default,,0000,0000,0000,,Na última palestra falamos sobre regressão linear e hoje eu quero falar sobre
Dialogue: 0,0:00:48.00,0:00:52.00,Default,,0000,0000,0000,,um tipo de adaptação dela chamada (locally weighted regression). É um algoritmo
Dialogue: 0,0:00:52.00,0:00:57.00,Default,,0000,0000,0000,,muito popular, na verdade este provavelmente é um dos algoritmos de aprendizado de máquina
Dialogue: 0,0:00:57.00,0:00:59.00,Default,,0000,0000,0000,,favoritos de um de meus antigos mentores. Então
Dialogue: 0,0:00:59.00,0:01:02.00,Default,,0000,0000,0000,,iremos falar sobre uma provável segunda interpretação da regressão linear
Dialogue: 0,0:01:02.00,0:01:07.00,Default,,0000,0000,0000,,E usá-la para nos mover ao nosso primeiro algoritmo de classificação
Dialogue: 0,0:01:07.00,0:01:08.00,Default,,0000,0000,0000,,que é regressão logística
Dialogue: 0,0:01:08.00,0:01:12.00,Default,,0000,0000,0000,,Tomaremos uma pequena digressão para falar sobre uma coisa chamada algoritmo 'perceptron'
Dialogue: 0,0:01:12.00,0:01:15.00,Default,,0000,0000,0000,,que é algo que nós iremos voltar a discutir, novamente, mais tarde neste trimestre
Dialogue: 0,0:01:15.00,0:01:17.00,Default,,0000,0000,0000,,e
Dialogue: 0,0:01:17.00,0:01:21.00,Default,,0000,0000,0000,,Se o tempo permitir, eu espero chegar ao método de Newton, o qual é um algoritmo para
Dialogue: 0,0:01:21.00,0:01:23.00,Default,,0000,0000,0000,,ajustar modelos de
Dialogue: 0,0:01:23.00,0:01:24.00,Default,,0000,0000,0000,,regressão logística.
Dialogue: 0,0:01:24.00,0:01:30.00,Default,,0000,0000,0000,,Então, recapitulando onde nós paramos na palestra anterior,
Dialogue: 0,0:01:30.00,0:01:33.00,Default,,0000,0000,0000,,lembrem-se que a notação que eu defini foi que
Dialogue: 0,0:01:33.00,0:01:35.00,Default,,0000,0000,0000,,eu usei isto
Dialogue: 0,0:01:35.00,0:01:37.00,Default,,0000,0000,0000,,X, i sobrescrito
Dialogue: 0,0:01:37.00,0:01:44.00,Default,,0000,0000,0000,,Y, i sobrescrito, para denotaro exemplo de treinamento i
Dialogue: 0,0:01:47.00,0:01:49.00,Default,,0000,0000,0000,,e
Dialogue: 0,0:01:49.00,0:01:51.00,Default,,0000,0000,0000,,quando estávamos falando sobre regressão linear
Dialogue: 0,0:01:51.00,0:01:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:01:52.00,0:01:54.00,Default,,0000,0000,0000,,ou método dos mínimos quadrados
Dialogue: 0,0:01:54.00,0:01:56.00,Default,,0000,0000,0000,,Nós usamos isto para denotar
Dialogue: 0,0:01:56.00,0:01:58.00,Default,,0000,0000,0000,,o valor predito resultado
Dialogue: 0,0:01:58.00,0:02:00.00,Default,,0000,0000,0000,,da aplicação de minha "hipótese" H
Dialogue: 0,0:02:00.00,0:02:02.00,Default,,0000,0000,0000,,na entrada Xi
Dialogue: 0,0:02:02.00,0:02:04.00,Default,,0000,0000,0000,,E minha hipótese
Dialogue: 0,0:02:04.00,0:02:06.00,Default,,0000,0000,0000,,era parametrizada pelo
Dialogue: 0,0:02:06.00,0:02:08.00,Default,,0000,0000,0000,,vetor de parâmetros theta
Dialogue: 0,0:02:08.00,0:02:13.00,Default,,0000,0000,0000,,E então dizemos que isto era igual à soma de j = 0 á n
Dialogue: 0,0:02:13.00,0:02:15.00,Default,,0000,0000,0000,,da multiplicação de theta j por xj
Dialogue: 0,0:02:15.00,0:02:19.00,Default,,0000,0000,0000,,i superscrito
Dialogue: 0,0:02:19.00,0:02:22.00,Default,,0000,0000,0000,,que é produto de theta transposto por X
Dialogue: 0,0:02:22.00,0:02:25.00,Default,,0000,0000,0000,,E temos a convenção que X
Dialogue: 0,0:02:25.00,0:02:29.00,Default,,0000,0000,0000,,zero subscrito, é igual a 1, Então isto conta para a intersecção no nosso
Dialogue: 0,0:02:29.00,0:02:31.00,Default,,0000,0000,0000,,modelo de regressão linear
Dialogue: 0,0:02:31.00,0:02:33.00,Default,,0000,0000,0000,,E o n minúsculo aqui
Dialogue: 0,0:02:33.00,0:02:36.00,Default,,0000,0000,0000,,era a notação que eu estava usando para
Dialogue: 0,0:02:36.00,0:02:40.00,Default,,0000,0000,0000,,o número de 'features' no meu conjunto de Treinamento. Ok? Então
Dialogue: 0,0:02:40.00,0:02:43.00,Default,,0000,0000,0000,,no exemplo quando tentamos predizer o preço de casas, nós tínhamos duas 'features, o tamanho
Dialogue: 0,0:02:43.00,0:02:45.00,Default,,0000,0000,0000,,da casa e o número de quartos
Dialogue: 0,0:02:45.00,0:02:50.00,Default,,0000,0000,0000,,Nós tínhamos duas features, portanto, n-zinho era igual a 2
Dialogue: 0,0:02:50.00,0:02:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:02:51.00,0:02:54.00,Default,,0000,0000,0000,,Então, apenas para
Dialogue: 0,0:02:54.00,0:02:57.00,Default,,0000,0000,0000,,terminar de recapitular a palestra anterior
Dialogue: 0,0:02:57.00,0:03:01.00,Default,,0000,0000,0000,,nós definimos esta função de custo quadrática J de theta
Dialogue: 0,0:03:01.00,0:03:05.00,Default,,0000,0000,0000,,era igual a 1/2, soma de i = 1 à m ....
Dialogue: 0,0:03:05.00,0:03:07.00,Default,,0000,0000,0000,,...
Dialogue: 0,0:03:07.00,0:03:10.00,Default,,0000,0000,0000,,...
Dialogue: 0,0:03:10.00,0:03:12.00,Default,,0000,0000,0000,,ao quadrdado
Dialogue: 0,0:03:12.00,0:03:16.00,Default,,0000,0000,0000,,onde esta é a soma sobre nossos m exemplos de treinamento e meu conjunto de treinamento. Então
Dialogue: 0,0:03:16.00,0:03:17.00,Default,,0000,0000,0000,,m minúsculo
Dialogue: 0,0:03:17.00,0:03:21.00,Default,,0000,0000,0000,,era a notação que eu estava usando para denotar o número de exemplos de treinamento que eu tinha
Dialogue: 0,0:03:21.00,0:03:23.00,Default,,0000,0000,0000,,e o tamanho do meu conjunto de treinamento
Dialogue: 0,0:03:23.00,0:03:25.00,Default,,0000,0000,0000,,E no fim da última palestra
Dialogue: 0,0:03:25.00,0:03:26.00,Default,,0000,0000,0000,,nós derivamos
Dialogue: 0,0:03:26.00,0:03:30.00,Default,,0000,0000,0000,,o valor de theta que minimiza isto na forma fechada, que era X
Dialogue: 0,0:03:30.00,0:03:32.00,Default,,0000,0000,0000,,transposto X
Dialogue: 0,0:03:32.00,0:03:35.00,Default,,0000,0000,0000,,inverso X transposto
Dialogue: 0,0:03:35.00,0:03:38.00,Default,,0000,0000,0000,,Y. Ok?
Dialogue: 0,0:03:38.00,0:03:42.00,Default,,0000,0000,0000,,Então
Dialogue: 0,0:03:42.00,0:03:46.00,Default,,0000,0000,0000,,A medida que continuarmos a palestra de hoje, Eu continuarei a usar esta anotação e, novamente,
Dialogue: 0,0:03:46.00,0:03:50.00,Default,,0000,0000,0000,,Eu percebo que é uma grande quantidade de notações para todos lembrarem,
Dialogue: 0,0:03:50.00,0:03:55.00,Default,,0000,0000,0000,,Então se durante esta palestra você se esquecer - se você tiver problemas para se lembrar
Dialogue: 0,0:03:55.00,0:04:02.00,Default,,0000,0000,0000,,o que m minúsculo é ou o que n minúsculo é ou qualquer coisa, por favor levante sua mão e pergunte.
Dialogue: 0,0:04:04.00,0:04:07.00,Default,,0000,0000,0000,,Quando nós falamos sobre regressão linear da última vez
Dialogue: 0,0:04:07.00,0:04:10.00,Default,,0000,0000,0000,,nós usamos duas features. Uma delas era
Dialogue: 0,0:04:10.00,0:04:14.00,Default,,0000,0000,0000,,o tamanho das casas em 'pés' quadrados, Então a área de convivência da casa
Dialogue: 0,0:04:14.00,0:04:18.00,Default,,0000,0000,0000,,e a outra feature era o número de quartos na casa
Dialogue: 0,0:04:18.00,0:04:22.00,Default,,0000,0000,0000,,Geralmente, nós aplicamos um algoritmo de aprendizado de máquina a um problema que
Dialogue: 0,0:04:22.00,0:04:23.00,Default,,0000,0000,0000,,importa para você
Dialogue: 0,0:04:23.00,0:04:28.00,Default,,0000,0000,0000,,A escolha de features irá depender muito de você, certo?
Dialogue: 0,0:04:28.00,0:04:32.00,Default,,0000,0000,0000,,E o modo como você escolhe suas features para dar ao algoritmo, frequentemente irá
Dialogue: 0,0:04:32.00,0:04:34.00,Default,,0000,0000,0000,,causar um grande impacto na maneira como ele funciona.
Dialogue: 0,0:04:34.00,0:04:40.00,Default,,0000,0000,0000,,Então, por exemplo
Dialogue: 0,0:04:40.00,0:04:44.00,Default,,0000,0000,0000,,a escolha que nós fizemos da última vez era x1 igual ao tamanho.
Dialogue: 0,0:04:44.00,0:04:47.00,Default,,0000,0000,0000,,e vamos deixar esta idéia de feature do número de quartos por enquanto. Vamos dizer que nós não temos dados
Dialogue: 0,0:04:47.00,0:04:50.00,Default,,0000,0000,0000,,que nos digam quantos quartos há nestas casas
Dialogue: 0,0:04:50.00,0:04:54.00,Default,,0000,0000,0000,,Uma coisa que você poderia fazer é definir - oh, vamos
Dialogue: 0,0:04:54.00,0:04:56.00,Default,,0000,0000,0000,,desenhar isto
Dialogue: 0,0:04:56.00,0:05:03.00,Default,,0000,0000,0000,,E então...
Dialogue: 0,0:05:04.00,0:05:07.00,Default,,0000,0000,0000,,Então digamos que isto era o tamanho da casa e que este é o preço da casa, então
Dialogue: 0,0:05:07.00,0:05:10.00,Default,,0000,0000,0000,,Se você usar
Dialogue: 0,0:05:10.00,0:05:14.00,Default,,0000,0000,0000,,isto como uma feature, talvez você tenha theta0 mais theta1
Dialogue: 0,0:05:14.00,0:05:19.00,Default,,0000,0000,0000,,x1, é um tipo de, modelo linear.
Dialogue: 0,0:05:19.00,0:05:21.00,Default,,0000,0000,0000,,Se você escolher - deixe-me apenas copiar
Dialogue: 0,0:05:21.00,0:05:26.00,Default,,0000,0000,0000,,os mesmos dados de novo, certo?
Dialogue: 0,0:05:26.00,0:05:30.00,Default,,0000,0000,0000,,Você pode definir o conjunto de features onde x1 é igual ao tamanho da casa
Dialogue: 0,0:05:30.00,0:05:34.00,Default,,0000,0000,0000,,e x2 é
Dialogue: 0,0:05:34.00,0:05:36.00,Default,,0000,0000,0000,,o quadrado
Dialogue: 0,0:05:36.00,0:05:37.00,Default,,0000,0000,0000,,do tamanho
Dialogue: 0,0:05:37.00,0:05:38.00,Default,,0000,0000,0000,,da casa, ok?
Dialogue: 0,0:05:38.00,0:05:43.00,Default,,0000,0000,0000,,Então x1 é o tamanho da casa em, digamos, uma medida quadrada (e.g. m²) e x2 é
Dialogue: 0,0:05:43.00,0:05:45.00,Default,,0000,0000,0000,,qualquer medida quadrada da casa e apenas
Dialogue: 0,0:05:45.00,0:05:49.00,Default,,0000,0000,0000,,eleve esse número ao quadrado, e isto seria outro modo de escolher uma feature
Dialogue: 0,0:05:49.00,0:05:51.00,Default,,0000,0000,0000,,e se você fizer isto então
Dialogue: 0,0:05:51.00,0:05:55.00,Default,,0000,0000,0000,,o mesmo algoritmo irá se ajustar
Dialogue: 0,0:05:55.00,0:05:59.00,Default,,0000,0000,0000,,a uma função quadrática para você
Dialogue: 0,0:05:59.00,0:06:01.00,Default,,0000,0000,0000,,theta2, x1 ao quadrado
Dialogue: 0,0:06:01.00,0:06:06.00,Default,,0000,0000,0000,,ok? Porque isto
Dialogue: 0,0:06:06.00,0:06:09.00,Default,,0000,0000,0000,,na verdade é x2. E
Dialogue: 0,0:06:09.00,0:06:12.00,Default,,0000,0000,0000,,dependendo da aparência dos dados, talvez isto se ajuste
Dialogue: 0,0:06:12.00,0:06:16.00,Default,,0000,0000,0000,,levemente melhor aos dados. Na verdade você pode levar isto
Dialogue: 0,0:06:16.00,0:06:23.00,Default,,0000,0000,0000,,ainda mais adiante
Dialogue: 0,0:06:25.00,0:06:26.00,Default,,0000,0000,0000,,O que é - vamos ver
Dialogue: 0,0:06:26.00,0:06:30.00,Default,,0000,0000,0000,,Eu tenho sete exemplos de treinamento aqui, então na verdade você pode
Dialogue: 0,0:06:30.00,0:06:34.00,Default,,0000,0000,0000,,,talvez, ajustar até um polinômio de grau 6. Você poderia ajustar um modelo
Dialogue: 0,0:06:34.00,0:06:35.00,Default,,0000,0000,0000,,theta0 mais
Dialogue: 0,0:06:35.00,0:06:38.00,Default,,0000,0000,0000,,theta1, x1 mais theta2
Dialogue: 0,0:06:38.00,0:06:42.00,Default,,0000,0000,0000,,x ao quadrado mais etc...
Dialogue: 0,0:06:42.00,0:06:48.00,Default,,0000,0000,0000,,até theta6. X à
Dialogue: 0,0:06:48.00,0:06:52.00,Default,,0000,0000,0000,,potência de 6 e theta é o polinômio
Dialogue: 0,0:06:52.00,0:06:55.00,Default,,0000,0000,0000,,para estes sete pontos de dados
Dialogue: 0,0:06:55.00,0:06:58.00,Default,,0000,0000,0000,,E se você fizer isto, você vai descobrir que
Dialogue: 0,0:06:58.00,0:07:01.00,Default,,0000,0000,0000,,você criou um modelo que se ajusta exatamente aos seus dados. É aí que, eu acho,
Dialogue: 0,0:07:01.00,0:07:06.00,Default,,0000,0000,0000,,neste exemplo que eu desenhei, nós temos 7 pontos de dados, então se você ajusta um
Dialogue: 0,0:07:06.00,0:07:08.00,Default,,0000,0000,0000,,modelo de polinômio de grau 6, você pode, meio que, ajustar
Dialogue: 0,0:07:08.00,0:07:11.00,Default,,0000,0000,0000,,uma linha que passa por estes sete pontos perfeitamente
Dialogue: 0,0:07:11.00,0:07:14.00,Default,,0000,0000,0000,,e você provavelmente encontra uma curva que
Dialogue: 0,0:07:14.00,0:07:17.00,Default,,0000,0000,0000,,você iria conseguir algo
Dialogue: 0,0:07:17.00,0:07:20.00,Default,,0000,0000,0000,,parecido com isto
Dialogue: 0,0:07:20.00,0:07:23.00,Default,,0000,0000,0000,,E por um lado, é um ótimo modelo no sentido que ele
Dialogue: 0,0:07:23.00,0:07:25.00,Default,,0000,0000,0000,,se ajusta perfeitamente aos dados de treinamento
Dialogue: 0,0:07:25.00,0:07:26.00,Default,,0000,0000,0000,,Mas por outro, este provavelmente não é
Dialogue: 0,0:07:26.00,0:07:28.00,Default,,0000,0000,0000,,um bom modelo no sentido que
Dialogue: 0,0:07:28.00,0:07:31.00,Default,,0000,0000,0000,,nenhum de nós, realmente pensa que esta seja uma boa predição dos preços
Dialogue: 0,0:07:31.00,0:07:36.00,Default,,0000,0000,0000,,das casas, como uma função do tamanho da casa, certo? Então
Dialogue: 0,0:07:36.00,0:07:39.00,Default,,0000,0000,0000,,Na verdade, nós voltaremos a isto mais tarde. Acontece que
Dialogue: 0,0:07:39.00,0:07:41.00,Default,,0000,0000,0000,,nos modelos que nós temos aqui;
Dialogue: 0,0:07:41.00,0:07:45.00,Default,,0000,0000,0000,,Eu sinto que talvez o modelo quadrático se ajuste melhor
Dialogue: 0,0:07:45.00,0:07:46.00,Default,,0000,0000,0000,,Enquanto que
Dialogue: 0,0:07:46.00,0:07:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:07:47.00,0:07:52.00,Default,,0000,0000,0000,,O modelo linear parece que possui um pouco de componente quadrático nestes
Dialogue: 0,0:07:52.00,0:07:52.00,Default,,0000,0000,0000,,dados
Dialogue: 0,0:07:52.00,0:07:56.00,Default,,0000,0000,0000,,que a função linear não está capturando
Dialogue: 0,0:07:56.00,0:07:59.00,Default,,0000,0000,0000,,Então nós iremos voltar a isto um pouco mais tarde e falar sobre os problemas
Dialogue: 0,0:07:59.00,0:08:03.00,Default,,0000,0000,0000,,associados com ajuste de modelos que são, ou muito simples, usam dos pequenos
Dialogue: 0,0:08:03.00,0:08:04.00,Default,,0000,0000,0000,,conjuntos de features, ou
Dialogue: 0,0:08:04.00,0:08:08.00,Default,,0000,0000,0000,,em modelos que são muito complexos e talvez
Dialogue: 0,0:08:08.00,0:08:11.00,Default,,0000,0000,0000,,usem um conjunto muito grande de featurees
Dialogue: 0,0:08:11.00,0:08:12.00,Default,,0000,0000,0000,,apenas para dar-lhes
Dialogue: 0,0:08:12.00,0:08:13.00,Default,,0000,0000,0000,,um nome,
Dialogue: 0,0:08:13.00,0:08:14.00,Default,,0000,0000,0000,,nós chamamos isto de
Dialogue: 0,0:08:14.00,0:08:19.00,Default,,0000,0000,0000,,o problema de 'underfitting' (subajuste)
Dialogue: 0,0:08:19.00,0:08:23.00,Default,,0000,0000,0000,,e, muito informalmente, isto se refere a uma configuração onde
Dialogue: 0,0:08:23.00,0:08:26.00,Default,,0000,0000,0000,,existem padrões óbvios que - onde há padrões nos dados que
Dialogue: 0,0:08:26.00,0:08:28.00,Default,,0000,0000,0000,,o algoritmo está falhando a se ajustar
Dialogue: 0,0:08:28.00,0:08:32.00,Default,,0000,0000,0000,,E a este problema aqui nós nos referimos como sendo
Dialogue: 0,0:08:32.00,0:08:34.00,Default,,0000,0000,0000,,overfitting (sobreajuste)
Dialogue: 0,0:08:34.00,0:08:36.00,Default,,0000,0000,0000,,e, novamente, muito informalmente,
Dialogue: 0,0:08:36.00,0:08:41.00,Default,,0000,0000,0000,,isto acontece quando o algoritmo está ajustando às Idiossincrasias de um conjunto de dados específicos
Dialogue: 0,0:08:41.00,0:08:43.00,Default,,0000,0000,0000,,certo? Isto acontece apenas porque
Dialogue: 0,0:08:43.00,0:08:48.00,Default,,0000,0000,0000,,das sete casas que tiramos amostras em Portland, ou de onde quer que você esteja coletando dados,
Dialogue: 0,0:08:48.00,0:08:51.00,Default,,0000,0000,0000,,esta casa acaba por ser um pouco mais cara, esta outro um pouco menos
Dialogue: 0,0:08:51.00,0:08:54.00,Default,,0000,0000,0000,,cara, e por
Dialogue: 0,0:08:54.00,0:08:57.00,Default,,0000,0000,0000,,ajustar seis ao polinômio nós, meio que, ajustamos características individuais
Dialogue: 0,0:08:57.00,0:08:58.00,Default,,0000,0000,0000,,deste conjunto de dados,
Dialogue: 0,0:08:58.00,0:09:01.00,Default,,0000,0000,0000,,Ao invés das verdadeiras tendências subjacentes
Dialogue: 0,0:09:01.00,0:09:04.00,Default,,0000,0000,0000,,de como o preço das casas varia como uma função do tamanho da casa. Ok?
Dialogue: 0,0:09:04.00,0:09:08.00,Default,,0000,0000,0000,,Então, estes são dois problemas muito diferentes. Nós iremos definí-los mais formalmente mais tarde.
Dialogue: 0,0:09:08.00,0:09:12.00,Default,,0000,0000,0000,,e falar sobre como atacar cada um destes problemas.
Dialogue: 0,0:09:12.00,0:09:13.00,Default,,0000,0000,0000,,Mas por agora eu
Dialogue: 0,0:09:13.00,0:09:20.00,Default,,0000,0000,0000,,espero apreciar que há este assunto de seleção de features.
Dialogue: 0,0:09:22.00,0:09:23.00,Default,,0000,0000,0000,,Então se você quiser apenas
Dialogue: 0,0:09:23.00,0:09:26.00,Default,,0000,0000,0000,,ensinar-nos os problemas de aprendizado, existem poucas maneiras de fazê-lo
Dialogue: 0,0:09:26.00,0:09:27.00,Default,,0000,0000,0000,,Então
Dialogue: 0,0:09:27.00,0:09:29.00,Default,,0000,0000,0000,,Nós iremos falar sobre
Dialogue: 0,0:09:29.00,0:09:32.00,Default,,0000,0000,0000,,algoritmos de seleção de features, mais tarde neste trimestre também. Então algoritmos automáticos
Dialogue: 0,0:09:32.00,0:09:33.00,Default,,0000,0000,0000,,para escolher
Dialogue: 0,0:09:33.00,0:09:35.00,Default,,0000,0000,0000,,que features usar em um
Dialogue: 0,0:09:35.00,0:09:37.00,Default,,0000,0000,0000,,problema de regressão como este
Dialogue: 0,0:09:37.00,0:09:41.00,Default,,0000,0000,0000,,O que eu quero fazer hoje é falar sobre uma classe de algoritmos
Dialogue: 0,0:09:41.00,0:09:44.00,Default,,0000,0000,0000,,chamados de algoritmos de aprendizado não paramétricos que irão ajudar
Dialogue: 0,0:09:44.00,0:09:49.00,Default,,0000,0000,0000,,de alguma forma, a aliviar a necessidade de você escolher features muito cuidadosamente. Ok?
Dialogue: 0,0:09:49.00,0:09:56.00,Default,,0000,0000,0000,,E isto nos leva a nossa discussão sobre (locally weighted regression).
Dialogue: 0,0:09:56.00,0:10:03.00,Default,,0000,0000,0000,,E só pra definir o termo
Dialogue: 0,0:10:12.00,0:10:16.00,Default,,0000,0000,0000,,A regressão linear, da medida que definimos até agora, é um exemplo de um algoritmo de aprendizado paramétrico
Dialogue: 0,0:10:16.00,0:10:17.00,Default,,0000,0000,0000,,e
Dialogue: 0,0:10:17.00,0:10:19.00,Default,,0000,0000,0000,,algoritmo de aprendizado paramétrico
Dialogue: 0,0:10:19.00,0:10:21.00,Default,,0000,0000,0000,,da maneira como é definido é
Dialogue: 0,0:10:21.00,0:10:24.00,Default,,0000,0000,0000,,um algoritmo que tem um número fixo de parâmetros
Dialogue: 0,0:10:24.00,0:10:27.00,Default,,0000,0000,0000,,Que se ajustam aos dados, Então
Dialogue: 0,0:10:27.00,0:10:28.00,Default,,0000,0000,0000,,Na regressão linear
Dialogue: 0,0:10:28.00,0:10:32.00,Default,,0000,0000,0000,,nós temos um conjunto fixo de parâmetros theta. Que deve
Dialogue: 0,0:10:32.00,0:10:39.00,Default,,0000,0000,0000,,se ajustar aos dados
Dialogue: 0,0:10:39.00,0:10:46.00,Default,,0000,0000,0000,,Em contraste, o que eu vou falar sobre agora é nosso primeiro algoritmo de aprendizado não-paramétrico.
Dialogue: 0,0:10:58.00,0:11:02.00,Default,,0000,0000,0000,,A definição formal, a qual não é muito intuitiva, eu substitui por uma
Dialogue: 0,0:11:02.00,0:11:04.00,Default,,0000,0000,0000,,segunda, digamos, mais
Dialogue: 0,0:11:04.00,0:11:06.00,Default,,0000,0000,0000,,intuitiva.
Dialogue: 0,0:11:06.00,0:11:10.00,Default,,0000,0000,0000,,O tipo de definição formal do algoritmo de aprendizado não paramétrico é de um algoritmo
Dialogue: 0,0:11:10.00,0:11:17.00,Default,,0000,0000,0000,,onde o número de paramâmetros
Dialogue: 0,0:11:18.00,0:11:22.00,Default,,0000,0000,0000,,cresce
Dialogue: 0,0:11:22.00,0:11:25.00,Default,,0000,0000,0000,,com M, com o tramanho do conjunto de treinamento. E geralmente é
Dialogue: 0,0:11:25.00,0:11:30.00,Default,,0000,0000,0000,,definido como um número de parâmetros cresce linearmente com o tamanho do conjunto de treinamento.
Dialogue: 0,0:11:30.00,0:11:32.00,Default,,0000,0000,0000,,Esta é a definição formal
Dialogue: 0,0:11:32.00,0:11:33.00,Default,,0000,0000,0000,,Uma
Dialogue: 0,0:11:33.00,0:11:36.00,Default,,0000,0000,0000,,definição levemente menos formal é que
Dialogue: 0,0:11:36.00,0:11:37.00,Default,,0000,0000,0000,,A quantidade de coisa que seu algoritmo de aprendizado precisa
Dialogue: 0,0:11:37.00,0:11:40.00,Default,,0000,0000,0000,,para se manter funcionando
Dialogue: 0,0:11:40.00,0:11:44.00,Default,,0000,0000,0000,,irá crescer linearmente com o conjunto de treinamento ou, outra forma de dizer isto, é um
Dialogue: 0,0:11:44.00,0:11:45.00,Default,,0000,0000,0000,,algoritmo que
Dialogue: 0,0:11:45.00,0:11:51.00,Default,,0000,0000,0000,,Nós iremos precisar manter o conjunto de treinamento inteiro, mesmo depois do aprendizado. Ok? Então
Dialogue: 0,0:11:51.00,0:11:53.00,Default,,0000,0000,0000,,Não se preocupe demais sobre esta definição. Mas
Dialogue: 0,0:11:53.00,0:11:55.00,Default,,0000,0000,0000,,O que eu quero agora é
Dialogue: 0,0:11:55.00,0:11:58.00,Default,,0000,0000,0000,,descrever um algoritmo de aprendizado não-paramétrico específico
Dialogue: 0,0:11:58.00,0:12:05.00,Default,,0000,0000,0000,,chamado "locally weighted regression"
Dialogue: 0,0:12:09.00,0:12:16.00,Default,,0000,0000,0000,,O qual também recebe um par de outros nomes
Dialogue: 0,0:12:17.00,0:12:20.00,Default,,0000,0000,0000,,O qual também é nomeado como Loess, por razões meio históricas. Loess
Dialogue: 0,0:12:20.00,0:12:23.00,Default,,0000,0000,0000,,Geralmente é soletrado L-O-E-S-S
Dialogue: 0,0:12:23.00,0:12:24.00,Default,,0000,0000,0000,,algumas vezes falando assim,
Dialogue: 0,0:12:24.00,0:12:27.00,Default,,0000,0000,0000,,também. Eu só chamo de "locally weighted regression"
Dialogue: 0,0:12:27.00,0:12:34.00,Default,,0000,0000,0000,,Então aqui está
Dialogue: 0,0:12:34.00,0:12:37.00,Default,,0000,0000,0000,,a idéia. Este será um algoritmo que vai nos permitir
Dialogue: 0,0:12:37.00,0:12:42.00,Default,,0000,0000,0000,,nos preocupar um pouco menos sobre ter que escolher features muito cuidadosamente.
Dialogue: 0,0:12:42.00,0:12:48.00,Default,,0000,0000,0000,,Então
Dialogue: 0,0:12:48.00,0:12:55.00,Default,,0000,0000,0000,,Para meu exemplo de motivação vamos dizer que eu
Dialogue: 0,0:12:55.00,0:12:59.00,Default,,0000,0000,0000,,tenho um
Dialogue: 0,0:12:59.00,0:13:00.00,Default,,0000,0000,0000,,conjunto de terinamento que parece com este, ok?
Dialogue: 0,0:13:00.00,0:13:04.00,Default,,0000,0000,0000,,Então isto é X e isto é Y
Dialogue: 0,0:13:04.00,0:13:07.00,Default,,0000,0000,0000,,Se você executar
Dialogue: 0,0:13:07.00,0:13:10.00,Default,,0000,0000,0000,,regressão linear nisto e você ajustar talvez, uma função linear a isto
Dialogue: 0,0:13:10.00,0:13:12.00,Default,,0000,0000,0000,,você acaba com uma
Dialogue: 0,0:13:12.00,0:13:13.00,Default,,0000,0000,0000,,mais ou menos
Dialogue: 0,0:13:13.00,0:13:16.00,Default,,0000,0000,0000,,linha reta, a qual não é um ajuste muito bom para esses dados.
Dialogue: 0,0:13:16.00,0:13:19.00,Default,,0000,0000,0000,,Você ainda pode sentar e encarar isto e tentar decidir que features são usadas certo
Dialogue: 0,0:13:19.00,0:13:22.00,Default,,0000,0000,0000,,Então talvez você queira jogar uma função quadrática
Dialogue: 0,0:13:22.00,0:13:25.00,Default,,0000,0000,0000,,Mas ela não é realmente quadrática também. Então talvez você queira
Dialogue: 0,0:13:25.00,0:13:27.00,Default,,0000,0000,0000,,modelar isto como um X
Dialogue: 0,0:13:27.00,0:13:31.00,Default,,0000,0000,0000,,mais X ao quadrado mas talvez alguma função do seno de X ou algo do tipo
Dialogue: 0,0:13:31.00,0:13:33.00,Default,,0000,0000,0000,,Na verdade você pode sentar e perder tempo com features
Dialogue: 0,0:13:33.00,0:13:37.00,Default,,0000,0000,0000,,E depois de um tempo você provavelmente virá com um conjunto de features cujo modelo está
Dialogue: 0,0:13:37.00,0:13:39.00,Default,,0000,0000,0000,,ok, mas vamos falar de um algoritmo que
Dialogue: 0,0:13:39.00,0:13:46.00,Default,,0000,0000,0000,,você pode usar sem necessidade de fazer isso
Dialogue: 0,0:13:50.00,0:13:52.00,Default,,0000,0000,0000,,Então
Dialogue: 0,0:13:52.00,0:13:54.00,Default,,0000,0000,0000,,Se - bem
Dialogue: 0,0:13:54.00,0:13:56.00,Default,,0000,0000,0000,,suponha que você quer avaliar
Dialogue: 0,0:13:56.00,0:13:59.00,Default,,0000,0000,0000,,sua hipótese H
Dialogue: 0,0:13:59.00,0:14:03.00,Default,,0000,0000,0000,,em um certo ponto.
Dialogue: 0,0:14:03.00,0:14:06.00,Default,,0000,0000,0000,,Com um certo ponto de requisição em X. ok? e
Dialogue: 0,0:14:06.00,0:14:07.00,Default,,0000,0000,0000,,vamos dizer que
Dialogue: 0,0:14:07.00,0:14:10.00,Default,,0000,0000,0000,,você quer saber qual o valor predito de
Dialogue: 0,0:14:10.00,0:14:11.00,Default,,0000,0000,0000,,Y
Dialogue: 0,0:14:11.00,0:14:16.00,Default,,0000,0000,0000,,nesta posição de X, certo? Então
Dialogue: 0,0:14:16.00,0:14:18.00,Default,,0000,0000,0000,,para a regressão linear
Dialogue: 0,0:14:18.00,0:14:22.00,Default,,0000,0000,0000,,O que nós estamos fazemos era ajustar
Dialogue: 0,0:14:22.00,0:14:25.00,Default,,0000,0000,0000,,theta
Dialogue: 0,0:14:25.00,0:14:28.00,Default,,0000,0000,0000,,para minimizar
Dialogue: 0,0:14:28.00,0:14:30.00,Default,,0000,0000,0000,,a soma sobre i
Dialogue: 0,0:14:30.00,0:14:34.00,Default,,0000,0000,0000,,Yi menos theta, transposto Xi
Dialogue: 0,0:14:34.00,0:14:38.00,Default,,0000,0000,0000,,ao quadrado e retornando theta
Dialogue: 0,0:14:38.00,0:14:41.00,Default,,0000,0000,0000,,transposto X. Ok?
Dialogue: 0,0:14:41.00,0:14:46.00,Default,,0000,0000,0000,,Então esta era regressão linear
Dialogue: 0,0:14:46.00,0:14:49.00,Default,,0000,0000,0000,,Em contraste, em "locally weighted linear regression" você irá fazer as coisas levemente
Dialogue: 0,0:14:49.00,0:14:51.00,Default,,0000,0000,0000,,diferente. ok?
Dialogue: 0,0:14:51.00,0:14:54.00,Default,,0000,0000,0000,,vamos olhar para este ponto X
Dialogue: 0,0:14:54.00,0:14:58.00,Default,,0000,0000,0000,,e então eu irei olhar no meu conjunto de dados e levar em consideração
Dialogue: 0,0:14:58.00,0:15:03.00,Default,,0000,0000,0000,,apenas o conjunto de pontos, meio que, em uma pequena vizinhança de X. Ok?
Dialogue: 0,0:15:03.00,0:15:07.00,Default,,0000,0000,0000,,Então nós iremos olhar onde Eu quero avaliar minha hipótese. Eu vou olhar
Dialogue: 0,0:15:07.00,0:15:10.00,Default,,0000,0000,0000,,apenas a vizinhança
Dialogue: 0,0:15:10.00,0:15:13.00,Default,,0000,0000,0000,,deste ponto. Onde eu quero avaliar minha hipótese
Dialogue: 0,0:15:13.00,0:15:16.00,Default,,0000,0000,0000,,E então eu vou pegar,
Dialogue: 0,0:15:16.00,0:15:19.00,Default,,0000,0000,0000,,digamos, apenas estes poucos pontos
Dialogue: 0,0:15:19.00,0:15:21.00,Default,,0000,0000,0000,,e Irei
Dialogue: 0,0:15:21.00,0:15:22.00,Default,,0000,0000,0000,,aplicar regressão linear
Dialogue: 0,0:15:22.00,0:15:26.00,Default,,0000,0000,0000,,para ajustar uma linha reta apenas para este subcojunto dos dados, ok? Eu
Dialogue: 0,0:15:26.00,0:15:29.00,Default,,0000,0000,0000,,irei usar este sub-termo subconjunto onde nós iremos voltar novamente
Dialogue: 0,0:15:29.00,0:15:32.00,Default,,0000,0000,0000,,Então nós temos este conjunto de dados e Eu ajustei uma linha reta
Dialogue: 0,0:15:32.00,0:15:36.00,Default,,0000,0000,0000,,a ele e talvez eu tenha uma linha como esta
Dialogue: 0,0:15:36.00,0:15:40.00,Default,,0000,0000,0000,,e O que eu irei fazer então é
Dialogue: 0,0:15:40.00,0:15:45.00,Default,,0000,0000,0000,,avaliar este valor particular na linha reta
Dialogue: 0,0:15:45.00,0:15:47.00,Default,,0000,0000,0000,,e este será o valor que eu retorno para meu algoritmo
Dialogue: 0,0:15:47.00,0:15:50.00,Default,,0000,0000,0000,,Eu acho que isto seria o valor predito
Dialogue: 0,0:15:50.00,0:15:53.00,Default,,0000,0000,0000,,para
Dialogue: 0,0:15:53.00,0:15:57.00,Default,,0000,0000,0000,,Isto seria o valor que minha hipótese retornaria
Dialogue: 0,0:15:57.00,0:16:04.00,Default,,0000,0000,0000,,em "locally weighted regression" ok? Então
Dialogue: 0,0:16:05.00,0:16:10.00,Default,,0000,0000,0000,,nós iremos seguir em frente. Deixe me continuar e formalizar isto
Dialogue: 0,0:16:10.00,0:16:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:15.00,0:16:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:18.00,0:16:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:27.00,0:16:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:32.00,0:16:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:36.00,0:16:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:37.00,0:16:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:41.00,0:16:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:42.00,0:16:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:45.00,0:16:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:49.00,0:16:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:54.00,0:16:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:16:57.00,0:17:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:06.00,0:17:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:10.00,0:17:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:14.00,0:17:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:15.00,0:17:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:19.00,0:17:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:21.00,0:17:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:25.00,0:17:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:27.00,0:17:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:30.00,0:17:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:31.00,0:17:34.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:34.00,0:17:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:42.00,0:17:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:43.00,0:17:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:48.00,0:17:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:51.00,0:17:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:51.00,0:17:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:17:56.00,0:18:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:02.00,0:18:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:05.00,0:18:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:12.00,0:18:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:13.00,0:18:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:16.00,0:18:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:21.00,0:18:24.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:24.00,0:18:28.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:28.00,0:18:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:32.00,0:18:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:35.00,0:18:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:37.00,0:18:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:39.00,0:18:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:44.00,0:18:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:47.00,0:18:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:48.00,0:18:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:54.00,0:18:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:55.00,0:18:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:18:58.00,0:19:00.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:00.00,0:19:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:02.00,0:19:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:05.00,0:19:06.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:06.00,0:19:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:10.00,0:19:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:17.00,0:19:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:22.00,0:19:26.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:26.00,0:19:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:29.00,0:19:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:33.00,0:19:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:35.00,0:19:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:37.00,0:19:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:41.00,0:19:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:42.00,0:19:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:45.00,0:19:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:47.00,0:19:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:49.00,0:19:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:51.00,0:19:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:53.00,0:19:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:55.00,0:19:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:19:59.00,0:20:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:02.00,0:20:06.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:06.00,0:20:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:07.00,0:20:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:12.00,0:20:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:13.00,0:20:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:16.00,0:20:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:21.00,0:20:23.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:23.00,0:20:28.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:28.00,0:20:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:32.00,0:20:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:33.00,0:20:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:36.00,0:20:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:39.00,0:20:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:42.00,0:20:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:44.00,0:20:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:48.00,0:20:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:50.00,0:20:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:53.00,0:20:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:54.00,0:20:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:56.00,0:20:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:20:58.00,0:21:01.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:01.00,0:21:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:04.00,0:21:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:07.00,0:21:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:10.00,0:21:11.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:11.00,0:21:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:15.00,0:21:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:17.00,0:21:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:21.00,0:21:24.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:24.00,0:21:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:27.00,0:21:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:32.00,0:21:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:33.00,0:21:40.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:40.00,0:21:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:47.00,0:21:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:49.00,0:21:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:53.00,0:21:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:55.00,0:21:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:21:57.00,0:22:00.00,Default,,0000,0000,0000,,
Dialogue: 0,0:22:00.00,0:22:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:22:04.00,0:22:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:22:08.00,0:22:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:22:09.00,0:22:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:22:17.00,0:22:21.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:28:11.00,0:28:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:28:15.00,0:28:19.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:29:21.00,0:29:25.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:29:41.00,0:29:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:29:48.00,0:29:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:29:51.00,0:29:57.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:30:13.00,0:30:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:30:17.00,0:30:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:30:19.00,0:30:22.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:30:23.00,0:30:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:30:27.00,0:30:31.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:30:34.00,0:30:37.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:30:39.00,0:30:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:30:42.00,0:30:46.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:31:07.00,0:31:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:31:09.00,0:31:13.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:31:14.00,0:31:16.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:31:48.00,0:31:51.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:32:05.00,0:32:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:32:09.00,0:32:15.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:36:48.00,0:36:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:36:54.00,0:36:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:36:55.00,0:36:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:36:58.00,0:37:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:02.00,0:37:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:05.00,0:37:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:10.00,0:37:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:14.00,0:37:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:19.00,0:37:23.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:23.00,0:37:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:27.00,0:37:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:29.00,0:37:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:30.00,0:37:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:40.00,0:37:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:44.00,0:37:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:48.00,0:37:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:49.00,0:37:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:37:54.00,0:37:56.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:38:02.00,0:38:06.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:06.00,0:38:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:07.00,0:38:11.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:11.00,0:38:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:13.00,0:38:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:16.00,0:38:19.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:38:25.00,0:38:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:27.00,0:38:31.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:38:34.00,0:38:40.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:38:43.00,0:38:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:38:46.00,0:38:49.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:38:52.00,0:38:57.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:39:03.00,0:39:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:08.00,0:39:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:09.00,0:39:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:36.00,0:39:38.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:39:41.00,0:39:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:43.00,0:39:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:48.00,0:39:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:50.00,0:39:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:39:54.00,0:39:57.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:40:11.00,0:40:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:14.00,0:40:18.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:40:32.00,0:40:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:36.00,0:40:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:39.00,0:40:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:42.00,0:40:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:46.00,0:40:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:49.00,0:40:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:40:50.00,0:40:57.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:41:10.00,0:41:14.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:41:26.00,0:41:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:30.00,0:41:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:32.00,0:41:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:36.00,0:41:40.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:40.00,0:41:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:45.00,0:41:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:47.00,0:41:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:51.00,0:41:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:41:55.00,0:41:57.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:42:02.00,0:42:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:05.00,0:42:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:10.00,0:42:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:13.00,0:42:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:15.00,0:42:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:18.00,0:42:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:30.00,0:42:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:31.00,0:42:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:33.00,0:42:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:36.00,0:42:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:37.00,0:42:40.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:40.00,0:42:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:46.00,0:42:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:42:58.00,0:43:01.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:43:02.00,0:43:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:09.00,0:43:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:13.00,0:43:20.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:20.00,0:43:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:27.00,0:43:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:29.00,0:43:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:33.00,0:43:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:36.00,0:43:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:39.00,0:43:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:43.00,0:43:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:46.00,0:43:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:53.00,0:43:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:43:57.00,0:44:01.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:44:06.00,0:44:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:09.00,0:44:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:14.00,0:44:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:16.00,0:44:23.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:24.00,0:44:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:35.00,0:44:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:38.00,0:44:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:39.00,0:44:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:43.00,0:44:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:44.00,0:44:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:47.00,0:44:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:52.00,0:44:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:44:53.00,0:45:00.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:45:12.00,0:45:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:16.00,0:45:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:21.00,0:45:28.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:32.00,0:45:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:46.00,0:45:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:50.00,0:45:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:53.00,0:45:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:45:58.00,0:46:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:02.00,0:46:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:22.00,0:46:26.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:26.00,0:46:26.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:26.00,0:46:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:33.00,0:46:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:35.00,0:46:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:43.00,0:46:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:44.00,0:46:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:48.00,0:46:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:50.00,0:46:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:55.00,0:46:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:56.00,0:46:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:46:58.00,0:47:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:06.00,0:47:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:09.00,0:47:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:10.00,0:47:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:12.00,0:47:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:14.00,0:47:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:17.00,0:47:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:17.00,0:47:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:21.00,0:47:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:21.00,0:47:26.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:26.00,0:47:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:30.00,0:47:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:33.00,0:47:34.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:34.00,0:47:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:39.00,0:47:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:42.00,0:47:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:45.00,0:47:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:47.00,0:47:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:51.00,0:47:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:47:52.00,0:47:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:43.00,0:48:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:51.00,0:48:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:52.00,0:48:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:53.00,0:48:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:55.00,0:48:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:48:57.00,0:49:01.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:01.00,0:49:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:05.00,0:49:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:09.00,0:49:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:12.00,0:49:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:14.00,0:49:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:17.00,0:49:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:19.00,0:49:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:22.00,0:49:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:33.00,0:49:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:38.00,0:49:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:43.00,0:49:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:43.00,0:49:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:46.00,0:49:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:50.00,0:49:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:53.00,0:49:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:49:57.00,0:50:01.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:01.00,0:50:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:04.00,0:50:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:07.00,0:50:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:10.00,0:50:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:14.00,0:50:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:15.00,0:50:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:18.00,0:50:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:21.00,0:50:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:25.00,0:50:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:29.00,0:50:34.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:34.00,0:50:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:37.00,0:50:40.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:40.00,0:50:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:44.00,0:50:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:50.00,0:50:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:55.00,0:50:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:50:59.00,0:51:06.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:06.00,0:51:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:08.00,0:51:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:14.00,0:51:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:15.00,0:51:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:18.00,0:51:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:21.00,0:51:25.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:51:26.00,0:51:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:30.00,0:51:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:31.00,0:51:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:32.00,0:51:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:36.00,0:51:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:37.00,0:51:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:41.00,0:51:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:47.00,0:51:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:51.00,0:51:53.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:53.00,0:51:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:55.00,0:51:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:51:58.00,0:52:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:02.00,0:52:02.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:52:04.00,0:52:06.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:52:11.00,0:52:16.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:16.00,0:52:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:17.00,0:52:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:19.00,0:52:21.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:21.00,0:52:26.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:26.00,0:52:31.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:52:33.00,0:52:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:39.00,0:52:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:43.00,0:52:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:45.00,0:52:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:50.00,0:52:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:52.00,0:52:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:55.00,0:52:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:56.00,0:52:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:52:59.00,0:53:01.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:01.00,0:53:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:04.00,0:53:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:07.00,0:53:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:10.00,0:53:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:13.00,0:53:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:15.00,0:53:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:26.00,0:53:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:30.00,0:53:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:31.00,0:53:36.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:36.00,0:53:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:38.00,0:53:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:42.00,0:53:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:47.00,0:53:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:49.00,0:53:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:51.00,0:53:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:54.00,0:53:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:53:57.00,0:54:00.00,Default,,0000,0000,0000,,
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Dialogue: 0,0:54:04.00,0:54:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:05.00,0:54:11.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:11.00,0:54:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:13.00,0:54:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:17.00,0:54:20.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:20.00,0:54:24.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:24.00,0:54:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:27.00,0:54:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:29.00,0:54:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:33.00,0:54:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:39.00,0:54:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:41.00,0:54:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:44.00,0:54:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:47.00,0:54:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:50.00,0:54:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:52.00,0:54:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:55.00,0:54:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:54:59.00,0:55:03.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:03.00,0:55:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:08.00,0:55:10.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:10.00,0:55:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:12.00,0:55:13.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:13.00,0:55:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:17.00,0:55:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:18.00,0:55:20.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:20.00,0:55:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:22.00,0:55:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:25.00,0:55:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:27.00,0:55:32.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:32.00,0:55:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:38.00,0:55:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:41.00,0:55:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:48.00,0:55:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:50.00,0:55:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:56.00,0:55:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:55:58.00,0:56:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:04.00,0:56:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:05.00,0:56:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:07.00,0:56:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:09.00,0:56:12.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:12.00,0:56:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:15.00,0:56:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:17.00,0:56:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:19.00,0:56:24.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:24.00,0:56:27.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:27.00,0:56:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:30.00,0:56:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:35.00,0:56:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:37.00,0:56:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:38.00,0:56:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:42.00,0:56:43.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:43.00,0:56:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:46.00,0:56:49.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:49.00,0:56:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:51.00,0:56:52.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:52.00,0:56:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:54.00,0:56:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:55.00,0:56:57.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:57.00,0:56:58.00,Default,,0000,0000,0000,,
Dialogue: 0,0:56:58.00,0:57:00.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:00.00,0:57:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:02.00,0:57:07.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:07.00,0:57:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:08.00,0:57:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:15.00,0:57:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:17.00,0:57:20.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:20.00,0:57:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:25.00,0:57:30.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:30.00,0:57:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:33.00,0:57:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:35.00,0:57:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:38.00,0:57:39.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:39.00,0:57:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:42.00,0:57:45.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:45.00,0:57:47.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:47.00,0:57:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:56.00,0:57:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:57:59.00,0:58:02.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:02.00,0:58:04.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:04.00,0:58:11.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:11.00,0:58:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:15.00,0:58:19.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:19.00,0:58:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:22.00,0:58:24.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:24.00,0:58:25.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:25.00,0:58:28.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:28.00,0:58:31.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:31.00,0:58:33.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:33.00,0:58:34.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:34.00,0:58:37.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:37.00,0:58:42.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:42.00,0:58:44.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:44.00,0:58:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:48.00,0:58:51.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:51.00,0:58:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:54.00,0:58:55.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:55.00,0:58:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:58:59.00,0:59:03.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:03.00,0:59:05.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:05.00,0:59:08.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:08.00,0:59:09.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:09.00,0:59:14.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:14.00,0:59:15.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:15.00,0:59:17.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:17.00,0:59:18.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:18.00,0:59:20.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:20.00,0:59:22.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:22.00,0:59:29.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:31.00,0:59:35.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:35.00,0:59:38.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:38.00,0:59:41.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:41.00,0:59:46.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:46.00,0:59:48.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:48.00,0:59:50.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:50.00,0:59:54.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:54.00,0:59:56.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:56.00,0:59:59.00,Default,,0000,0000,0000,,
Dialogue: 0,0:59:59.00,1:00:01.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:01.00,1:00:08.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:09.00,1:00:16.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:17.00,1:00:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:19.00,1:00:26.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:29.00,1:00:35.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:35.00,1:00:42.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:51.00,1:00:53.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:53.00,1:00:57.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:57.00,1:00:58.00,Default,,0000,0000,0000,,
Dialogue: 0,1:00:58.00,1:01:03.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:03.00,1:01:08.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:08.00,1:01:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:09.00,1:01:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:11.00,1:01:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:14.00,1:01:17.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:17.00,1:01:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:19.00,1:01:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:20.00,1:01:22.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:22.00,1:01:24.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:24.00,1:01:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:27.00,1:01:34.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:58.00,1:01:59.00,Default,,0000,0000,0000,,
Dialogue: 0,1:01:59.00,1:02:03.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:03.00,1:02:05.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:05.00,1:02:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:11.00,1:02:18.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:21.00,1:02:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:27.00,1:02:28.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:28.00,1:02:32.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:32.00,1:02:34.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:34.00,1:02:37.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:37.00,1:02:40.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:40.00,1:02:41.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:41.00,1:02:42.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:42.00,1:02:45.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:45.00,1:02:48.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:48.00,1:02:48.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:48.00,1:02:51.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:51.00,1:02:54.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:54.00,1:02:56.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:56.00,1:02:58.00,Default,,0000,0000,0000,,
Dialogue: 0,1:02:58.00,1:03:01.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:01.00,1:03:03.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:03.00,1:03:08.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:08.00,1:03:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:09.00,1:03:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:13.00,1:03:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:14.00,1:03:16.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:16.00,1:03:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:20.00,1:03:23.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:23.00,1:03:24.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:24.00,1:03:25.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:25.00,1:03:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:27.00,1:03:32.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:32.00,1:03:35.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:35.00,1:03:36.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:36.00,1:03:41.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:41.00,1:03:43.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:43.00,1:03:48.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:48.00,1:03:52.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:52.00,1:03:58.00,Default,,0000,0000,0000,,
Dialogue: 0,1:03:58.00,1:04:00.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:00.00,1:04:02.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:02.00,1:04:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:09.00,1:04:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:13.00,1:04:17.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:17.00,1:04:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:19.00,1:04:21.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:21.00,1:04:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:27.00,1:04:29.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:29.00,1:04:36.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:45.00,1:04:47.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:47.00,1:04:50.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:50.00,1:04:53.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:53.00,1:04:55.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:55.00,1:04:57.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:57.00,1:04:58.00,Default,,0000,0000,0000,,
Dialogue: 0,1:04:58.00,1:05:00.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:00.00,1:05:04.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:04.00,1:05:06.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:06.00,1:05:08.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:08.00,1:05:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:13.00,1:05:15.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:15.00,1:05:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:19.00,1:05:21.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:21.00,1:05:24.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:24.00,1:05:25.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:25.00,1:05:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:27.00,1:05:34.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:46.00,1:05:50.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:50.00,1:05:56.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:56.00,1:05:59.00,Default,,0000,0000,0000,,
Dialogue: 0,1:05:59.00,1:06:02.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:02.00,1:06:06.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:06.00,1:06:07.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:07.00,1:06:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:11.00,1:06:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:13.00,1:06:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:19.00,1:06:23.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:23.00,1:06:25.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:25.00,1:06:30.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:30.00,1:06:33.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:33.00,1:06:40.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:44.00,1:06:46.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:46.00,1:06:48.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:48.00,1:06:49.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:49.00,1:06:51.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:51.00,1:06:54.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:54.00,1:06:55.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:55.00,1:06:58.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:58.00,1:06:59.00,Default,,0000,0000,0000,,
Dialogue: 0,1:06:59.00,1:07:03.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:03.00,1:07:06.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:06.00,1:07:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:09.00,1:07:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:11.00,1:07:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:14.00,1:07:17.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:17.00,1:07:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:20.00,1:07:23.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:23.00,1:07:25.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:25.00,1:07:29.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:29.00,1:07:32.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:32.00,1:07:34.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:34.00,1:07:35.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:35.00,1:07:39.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:39.00,1:07:42.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:42.00,1:07:44.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:44.00,1:07:48.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:48.00,1:07:51.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:51.00,1:07:56.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:56.00,1:07:59.00,Default,,0000,0000,0000,,
Dialogue: 0,1:07:59.00,1:08:01.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:01.00,1:08:02.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:02.00,1:08:05.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:05.00,1:08:06.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:06.00,1:08:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:09.00,1:08:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:13.00,1:08:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:14.00,1:08:18.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:18.00,1:08:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:20.00,1:08:23.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:23.00,1:08:26.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:08:32.00,1:08:36.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:36.00,1:08:40.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:40.00,1:08:41.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:41.00,1:08:44.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:44.00,1:08:45.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:45.00,1:08:49.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:49.00,1:08:53.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:53.00,1:08:55.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:55.00,1:08:57.00,Default,,0000,0000,0000,,
Dialogue: 0,1:08:57.00,1:09:02.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:02.00,1:09:06.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:06.00,1:09:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:09.00,1:09:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:11.00,1:09:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:14.00,1:09:17.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:17.00,1:09:19.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:19.00,1:09:22.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:22.00,1:09:26.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:26.00,1:09:28.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:28.00,1:09:32.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:32.00,1:09:36.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:36.00,1:09:39.00,Default,,0000,0000,0000,,
Dialogue: 0,1:09:39.00,1:09:46.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:10:01.00,1:10:08.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:10:13.00,1:10:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:13.00,1:10:16.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:16.00,1:10:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:20.00,1:10:23.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:10:27.00,1:10:27.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:27.00,1:10:30.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:10:32.00,1:10:37.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:37.00,1:10:38.00,Default,,0000,0000,0000,,
Dialogue: 0,1:10:38.00,1:10:39.00,Default,,0000,0000,0000,,
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Dialogue: 0,1:11:07.00,1:11:13.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:13.00,1:11:14.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:14.00,1:11:17.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:17.00,1:11:20.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:20.00,1:11:21.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:21.00,1:11:24.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:24.00,1:11:28.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:28.00,1:11:30.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:30.00,1:11:32.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:32.00,1:11:39.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:44.00,1:11:49.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:49.00,1:11:51.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:51.00,1:11:53.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:53.00,1:11:56.00,Default,,0000,0000,0000,,
Dialogue: 0,1:11:56.00,1:12:00.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:00.00,1:12:01.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:01.00,1:12:05.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:05.00,1:12:09.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:09.00,1:12:11.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:11.00,1:12:18.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:19.00,1:12:23.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:23.00,1:12:25.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:25.00,1:12:28.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:28.00,1:12:31.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:31.00,1:12:33.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:33.00,1:12:36.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:36.00,1:12:41.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:41.00,1:12:42.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:42.00,1:12:45.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:45.00,1:12:49.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:49.00,1:12:54.00,Default,,0000,0000,0000,,
Dialogue: 0,1:12:54.00,1:12:56.00,Default,,0000,0000,0000,,