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machinelrn에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 machinelrn 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Machine Learning For Kids

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Manage episode 194063240 series 1828621
machinelrn에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 machinelrn 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Joined today by the world’s youngest machine learning engineer! She was inspired by “Age of Ultron” But how does machine learning actually work? We followed up this podcast with the Teachable Machine project based on a new library called deeplearn.js, which makes it easier for any web dev to get into machine learning. ML relies on specific representation of data, a set of features that are understandable for a computer. If we’re talking about text it should be represented through the words it contains or some other characteristics such as length of the text etc. All ML tasks can be classified in several categories, the main ones are: • Supervised ML • Unsupervised ML • Reinforcement learning. Supervised ML relies on data where the true label/class was indicated. This is easier to explain using an example. Let us imagine that we want to teach a computer to distinguish pictures of cats and dogs. We can ask some of our friends to send us pictures of cats and dogs adding a tag Cat or Dog. Follow-up questions: • Why did they want better machines? • How do you imagine and build something that doesn’t exist yet?
  continue reading

7 에피소드

Artwork
icon공유
 
Manage episode 194063240 series 1828621
machinelrn에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 machinelrn 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Joined today by the world’s youngest machine learning engineer! She was inspired by “Age of Ultron” But how does machine learning actually work? We followed up this podcast with the Teachable Machine project based on a new library called deeplearn.js, which makes it easier for any web dev to get into machine learning. ML relies on specific representation of data, a set of features that are understandable for a computer. If we’re talking about text it should be represented through the words it contains or some other characteristics such as length of the text etc. All ML tasks can be classified in several categories, the main ones are: • Supervised ML • Unsupervised ML • Reinforcement learning. Supervised ML relies on data where the true label/class was indicated. This is easier to explain using an example. Let us imagine that we want to teach a computer to distinguish pictures of cats and dogs. We can ask some of our friends to send us pictures of cats and dogs adding a tag Cat or Dog. Follow-up questions: • Why did they want better machines? • How do you imagine and build something that doesn’t exist yet?
  continue reading

7 에피소드

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