# Artificial Neural Networks and Deep Learning | Two Minute Papers #3

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=rCWTOOgVXyE
- **Дата:** 14.08.2015
- **Длительность:** 2:19
- **Просмотры:** 21,788
- **Источник:** https://ekstraktznaniy.ru/video/14970

## Описание

Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance.

Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks. These machine learning techniques proved to be useful for many tasks beyond image recognition: they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others.

If you would like to know more about neural networks and deep learning, make sure to check out these talks from 

## Транскрипт

### Segment 1 (00:00 - 02:00) []

I am Károly Zsolnai-Fehér, and this is Two Minute Papers, where I explain awesome research in simpler words. First of all, I am very happy to see that you like the series. Also, thanks for sharing it on social media sites, and please, keep 'em coming. This episode is going to be about artificial neural networks. I will quickly explain what the huge deep learning rage is all about. This graph depicts a neural network that we build and simulate on a computer. It is a very crude approximation of the human brain. The leftmost layer denotes inputs, which can be, for instance, the pixels of an input image. The rightmost layer is the output, which can be, for instance, a decision, whether the image depicts a horse or not. After we have given many inputs to the neural network, in its hidden layers, it will learn to figure out a way to recognize different classes of inputs, such as horses, people or school buses. What is really surprising is that it's quite faithful to the way the brain does represent objects on a lower level. It has a very similar edge detector. And, it also works for audio: Here you can find the difference between the neurons in the hearing system of a cat, versus a simulated neural network on the same audio signals. I mean, come on, this is amazing! What is the deep learning part of it all? Well it means that our neural network has multiple hidden layers on top of each other. The first layer for an image consists of edges, and as we go up, a combination of edges gives us object parts. A combination of object parts yield objects models, and so on. This kind of hierarchy provides us very powerful capabilities. For instance, in this traffic sign recognition contest, the second place was taken by humans, but what's more interesting, is that the first place was not taken by humans, it was taken a by a neural network algorithm. Think about that, and if you find these topics interesting, you feel you would like to hear about the newest research discoveries in an understandable way, please become a fellow scholar, and hit that subscribe button. And for now, thanks for watching, and I'll see you next time!