what-physics-can-teach-us-about-learning.json

{
  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
  "description": "Why do convolutional networks work well for images? What happens in a neural\nnetwork when it 'learns\u2019? What is machine learning, actually? These are the\ntype of questions that we should all be wondering about if we use machine\nlearning, and especially deep neural networks, on a daily basis. The field\nof deep learning is developing rapidly with new architectures being invented\nto try to solve ever more challenging problems, and this zoo of neural\nnetworks needs a taxonomy.\n\nOne way to bring order to the chaos is by using a physicist's intuition.\nBridges are being built, formalizing the link between well-developed fields\nin physics and neural networks, which allow us to understand extracting\ninformation relevant on the macroscopic scale as both a machine learning\nproblem and a problem that has been known in the physics community for a\nlong time, namely describing physical systems at different length scales.",
  "duration": 1500,
  "language": "eng",
  "recorded": "2019-10-04",
  "related_urls": [
    "https://2019.pygotham.org/talks/what-physics-can-teach-us-about-learning/"
  ],
  "speakers": [
    "Marianne Hoogeveen"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/ttnvU1QmPzc/maxresdefault.jpg",
  "title": "What physics can teach us about learning",
  "videos": [
    {
      "type": "youtube",
      "url": "https://youtu.be/ttnvU1QmPzc"
    }
  ]
}