Row 69674

Row ID: 69674 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 69674 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Coming from the perspective of a researcher, all of these things that you have mentioned are indeed critical to machine learning. They are the underlying building blocks for which AI is built on top of. In your career, you might never have to do Gauss Jordan elimination ever again. In fact, I would be really surprised if these concepts came up again outside of the setting of your linear algebra course. That doesn't mean that they aren't important - they are critically important. Somebody spent their career optimizing these things and implementing them into libraries so that the next generation could use them without thinking about it. Why should we care about learning these concepts then? This will likely be the only time in your life where you study these concepts at this level of granularity. The fact is, machine learning and AI are built on top of many different fields and it would be impossible to study them all in a single lifetime. However, the insights that we gain from thinking about building blocks influences our future thoughts and gives us a unique perspective on the world. Foundations allow us to draw from problems past to solve new ones. Think of these concepts as fractions of a percent toward your overall learning. One of two concepts on its own don't contribute much, but over time paying attention to these details will put you miles ahead of your peers.

FieldValue
text Coming from the perspective of a researcher, all of these things that you have mentioned are indeed critical to machine learning. They are the underlying building blocks for which AI is built on top of. In your career, you might never have to do Gauss Jordan elimination ever again. In fact, I would be really surprised if these concepts came up again outside of the setting of your linear algebra course. That doesn't mean that they aren't important - they are critically important. Somebody spent t…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
username_encoded Z0FBQUFBQm5Lak1lV2w5di10Q3NWbFd5STYxdm50WWJONXJLbXl4YTFScVNZWTlRUFZfd19GQ3JBeUhBS2pFMjNQbjJTNXcwU3B1dXQxbFlsWHFrWUZNMVRWQ2xtWGVoZ3c9PQ==
url_encoded Z0FBQUFBQm5Lak92NGl5emYyZUJrSllncy1DM29ZRkdfamhQQVFPSHRuYmdyelZWbzdFVUtCTUZ5YXAwc0tMQkQ1VnZDb3owemJYcHRLMWZQSS1qZklRczE5VFFHWTlkcUlEVC1QUUR1SDhPNFdpTk41LWJtZEJCM0M3TlcwUjdZLWxQclo4a3BDeGhKMGdZQ3JjX1dMNnRvaERxdlRUaWxTWUdUalRQcDhGblNUejcxUlloc3U5MmNpWDJvaE1LVVlBUzFWblROVEJM

Raw Record

{
  "text": "Coming from the perspective of a researcher, all of these things that you have mentioned are indeed critical to machine learning. They are the underlying building blocks for which AI is built on top of. In your career, you might never have to do Gauss Jordan elimination ever again. In fact, I would be really surprised if these concepts came up again outside of the setting of your linear algebra course. That doesn't mean that they aren't important - they are critically important. Somebody spent their career optimizing these things and implementing them into libraries so that the next generation could use them without thinking about it. Why should we care about learning these concepts then? This will likely be the only time in your life where you study these concepts at this level of granularity. The fact is, machine learning and AI are built on top of many different fields and it would be impossible to study them all in a single lifetime. However, the insights that we gain from thinking about building blocks influences our future thoughts and gives us a unique perspective on the world. Foundations allow us to draw from problems past to solve new ones. Think of these concepts as fractions of a percent toward your overall learning. One of two concepts on its own don't contribute much, but over time paying attention to these details will put you miles ahead of your peers.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1lV2w5di10Q3NWbFd5STYxdm50WWJONXJLbXl4YTFScVNZWTlRUFZfd19GQ3JBeUhBS2pFMjNQbjJTNXcwU3B1dXQxbFlsWHFrWUZNMVRWQ2xtWGVoZ3c9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak92NGl5emYyZUJrSllncy1DM29ZRkdfamhQQVFPSHRuYmdyelZWbzdFVUtCTUZ5YXAwc0tMQkQ1VnZDb3owemJYcHRLMWZQSS1qZklRczE5VFFHWTlkcUlEVC1QUUR1SDhPNFdpTk41LWJtZEJCM0M3TlcwUjdZLWxQclo4a3BDeGhKMGdZQ3JjX1dMNnRvaERxdlRUaWxTWUdUalRQcDhGblNUejcxUlloc3U5MmNpWDJvaE1LVVlBUzFWblROVEJM"
}
Explore Dataset Explore Row

Entry Information