Prática de Shadowing: A Gentle Introduction to Machine Learning - Aprenda a falar inglês com vídeo
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Gonna start this StatQuest with a silly song.
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But if you don't like silly songs, that's okay.
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StatQuest.
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Hello, I'm Josh Starmer and welcome to StatQuest.
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Today we're going to do a gentle introduction to machine learning.
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Note, this StatQuest was originally prepared for and presented at the Society for Scientific Advancement's annual conference.
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One of the things that SOSA does is promote science and technology in Jamaica.
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Let's start with a silly example.
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Do you like silly songs?
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If you like silly songs, are you interested in machine learning?
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If you like silly songs and machine learning, then you'll love StatQuest.
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If you like silly songs but not machine learning, are you interested in statistics?
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If you like silly songs and statistics but not machine learning, then you'll still love StatQuest.
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Otherwise, you might not like StatQuest.
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If you don't like silly songs, are you interested in machine learning?
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If you don't like silly songs but you like machine learning, then you'll love StatQuest.
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If you don't like silly songs or machine learning, are you interested in statistics?
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If you don't like silly songs or machine learning, but you're interested in statistics, then you will love StatQuest.
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Otherwise, you might not like StatQuest.
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Wah wah.
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This is a silly example, but it illustrates a decision tree, a simple machine learning method.
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The purpose of this particular decision tree is to predict whether or not someone will love StatQuest.
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Alternatively, we could say that this decision tree classifies a person as either someone who loves StatQuest or someone who doesn't.
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Since decision trees are a type of machine learning, then if you understand how we use this tree to predict or classify if someone would love StatQuest,
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you are well on your way to understanding machine learning.
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Bam!
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Here's another silly example of machine learning.
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Imagine we measured how quickly someone could run 100 meters.
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And how much yam they ate.
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This is me.
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I'm not very fast, and I don't eat much yam.
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These are some other people.
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And this is Usain Bolt.
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Usain Bolt is very fast, and he eats a lot of yam.
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Given this pretend data, we see that the more yam someone eats, the faster they run the 100 meter dash.
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We can fit a black line to the data to show the trend.
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but we can also use the black line to make predictions.
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For example, if someone told us they ate this much yam, then we could use the black line to predict how fast that person might run.
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This is the predicted speed.
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The black line is a type of machine learning because we can use it to make predictions.
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In general, machine learning is all about making predictions and classifications.
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Bam!
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Now that we can make predictions and classifications, let's talk about some of the main ideas in machine learning.
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First of all, in machine learning lingo, the original data is called training data.
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So the black line is fit to training data.
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Alternatively, we could have fit a green squiggle to the training data.
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The green squiggle fits the training data better than the black line, but remember, the goal of machine learning is to make predictions.
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So we need a way to decide if the green squiggle is better or worse than the black line at making predictions.
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So we find a new person and measure how fast they run and how much yam they eat.
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And then we find another, and another, and another.
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All together, the blue dots represent testing data.
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We use the testing data to compare the predictions made by the black line to the predictions made by the green squiggle.
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Let's start by seeing how well the black line predicts the speed of each person in the testing data.
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Here's the first person in the testing data.
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They ate this much yam.
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And they ran this fast.
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However, the black line predicts that someone who ate this much yam should run a little slower.
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So let's measure the distance between the actual speed and the predicted speed,
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and save the distance on the right while we focus on the other people in the testing data.
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Here's the second person in the testing data.
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They ate this much yam, and they ran this fast.
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but the black line predicts that they will run a little faster.
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So we measure the distance between the actual speed and the predicted speed, and add it to the one we measured for the first person in the testing data.
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Then we measure the distance between the real and the predicted speed for the third person in the testing data,
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and add it to our running total of distances between the real and predicted speeds for the black line.
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Then we do the same thing for the fourth person in the testing data.
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And add that distance to our running total for the black line.
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This is the sum of all the distances between the real and predicted speeds for the black line.
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Now let's calculate the distances between the real and predicted speeds using the green squiggle.
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Remember, the green squiggle did a great job fitting the training data.
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But when we are doing machine learning, we are more interested in how well the green squiggle can make predictions with new data.
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So, just like before, we determine this person's real speed and their predicted speed and measure the distance between them.
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And just like we did for the black line, We'll keep track of the distances for the green squiggle over here.
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Then we do the same thing for the second person in the testing data. And the third person. And the fourth person.
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This is the sum of the distances between the real and predicted speeds for the green squiggle.
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The sum of the distances is larger for the green squiggle than the black line.
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In other words, even though the green squiggle fit the training data way better than the black line,
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the black line did a better job predicting speeds with the testing data.
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So if we had to choose between using the black line or the green squiggle to make predictions, we would choose the black line.
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Bam!
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This example teaches two main ideas about machine learning.
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First, we use testing data to evaluate machine learning methods.
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Second, don't be fooled by how well a machine learning method fits the training data.
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Fitting the training data well but making poor predictions is called the bias-variance trade-off.
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Oh no!
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A shameless self-promotion!
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If you want to learn more about the bias-variance trade-off, there's a stat quest that will walk you through it one step at a time.
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Before we move on, you may be wondering why we used a simple black line
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in a silly green squiggle instead of a Deep Learning Convolutional Neural Network.
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Or, Insert newest, bestest, most fancy machine learning method here.
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There are tons of fancy-sounding machine learning methods.
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And each year, something new and exciting comes on the scene.
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But regardless of what you use, the most important thing isn't how fancy it is, but how it performs with testing data.
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Double bam!
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Now let's go back to the decision tree that we started with.
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Remember, we wanted to classify if someone loves StatQuest based on a few questions.
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To create the decision tree, we collected data from people who love StatQuest, and from people who did not love StatQuest.
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Altogether, this was the training data, and we used it to build the decision tree.
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Then we got data from a few more people who love StatQuest, and a few more people who did not love StatQuest.
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Altogether, this forms the testing data.
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We can use the testing data to see how well our decision tree predicts if someone will love StatQuest.
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The first person in the testing data did not like silly songs, so we go to the right side of the decision tree.
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They didn't like machine learning, either.
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So we just keep on going down the right side of the decision tree.
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They didn't like statistics either.
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So the decision tree predicts that this person will not love StatQuest.
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However, this person loves StatQuest, so the decision tree made a mistake.
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Wah wah.
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The second person in the testing data liked silly songs.
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And that takes us down the left side of the decision tree.
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They were also interested in machine learning.
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So we predict that that person loves StatQuest.
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And since this person actually loves StatQuest, the decision tree did a good job.
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Hooray!
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Now we just run all of the other people in the testing data down the decision tree
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and compare the predictions to reality.
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Then we can compare this decision tree to the latest greatest machine learning method.
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Ultimately, we pick the method that does the best job predicting if someone will love StatQuest or not.
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Triple Bam!
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In summary, machine learning is all about making predictions and classifications.
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There are tons of fancy machine learning methods, but the most important thing to know about them isn't what makes them so fancy.
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It's that we decide which method fits our needs the best by using testing data.
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One last thing before we go.
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You may be wondering how we decide which data go into the training set and which data go into the testing set.
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Earlier, we just arbitrarily decided that these red dots were the training data.
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But the blue dots could have, just as easily, been the training data.
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The good news is that there are ways to determine
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which samples should be used for training data and which samples should be used for testing data.
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And if you're interested in learning more about this, check out the StatQuest.
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And there are lots more StatQuests that walk you through machine learning concepts step by step, so check them out.
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Hooray!
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We've made it to the end of another exciting StatQuest.
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If you like this StatQuest and want to see more, please subscribe.
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And if you want to support StatQuest, well, consider buying one or two of my original songs, or getting a t-shirt or a hoodie or some other slick merchandise.
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There's links on the screen and there's links in the description below.
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Alright, until next time, quest on!
Contexto & Antecedentes
O vídeo "A Gentle Introduction to Machine Learning" com Josh Starmer apresenta uma introdução acessível ao campo do aprendizado de máquina. Durante a apresentação, Josh utiliza um tom leve e divertido, tornando tema complexo mais palatável. Ele fala sobre a importância de entender conceitos básicos como árvores de decisão e regressão, ilustrando-os com exemplos simples, como a relação entre o consumo de inhame e a velocidade de corrida. Isso não só ajuda na compreensão do aprendizado de máquina, mas também é um recurso útil para quem deseja aprender inglês com YouTube, especialmente no contexto de terminologias técnicas.
Top 5 Frases para Comunicação Diária
- “Gonna start this StatQuest with a silly song.” - Usar humor para iniciar uma conversa pode quebrar o gelo.
- “Do you like silly songs?” - Uma pergunta simples que pode iniciar um diálogo.
- “If you like silly songs and machine learning, then you'll love StatQuest.” - Expressar suas preferências pode ajudar na conexão.
- “The purpose of this particular decision tree is to predict whether or not someone will love StatQuest.” - Explicar objetivos de forma clara é essencial na comunicação.
- “The more yam someone eats, the faster they run.” - Usar analogias simples facilita a compreensão de conceitos.
Guia Passo a Passo para Shadowing
O shadowing em inglês é uma técnica eficaz para melhorar a pronúncia e a fluência. Aqui está um guia passo a passo baseado no vídeo:
- Escute com Atenção: Ouça o vídeo algumas vezes para se familiarizar com a entonação e o ritmo de Josh. Preste atenção particular às frases que você gostaria de praticar.
- Repita em Voz Alta: Após ouvir uma frase, pause o vídeo e tente repeti-la imitando exatamente o tom e a pronúncia. Isso ajuda a melhorar a pronúncia em inglês.
- Grave-se: Use seu celular ou um gravador para registrar sua voz enquanto faz o shadow speech. Isso permitirá que você compare sua pronúncia com a do falante nativo.
- Identifique Melhorias: Ouça suas gravações e identifique áreas a aprimorar. Concentre-se nos sons que você acha mais desafiadores.
- Pratique Regularmente: Faça dessa prática uma rotina diária. A repetição é fundamental para solidificar o aprendizado e melhorar a prática de conversação em inglês.
Seguindo esses passos, você não só melhorará sua pronúncia, mas também ganhará confiança ao se comunicar em inglês. Aplique essas técnicas e veja sua habilidade em inglês se desenvolver rapidamente!
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.