neural networks [2.1] : training neural networks - empirical risk minimization
Published 10 years ago • 54K plays • Length 10:28Download video MP4
Download video MP3
Similar videos
-
![neural networks [2.6] : training neural networks - parameter gradient](https://i.ytimg.com/vi/p5tL2JqCRDo/mqdefault.jpg)
6:26
neural networks [2.6] : training neural networks - parameter gradient
-
![neural networks [2.2] : training neural networks - loss function](https://i.ytimg.com/vi/PpFTODTztsU/mqdefault.jpg)
4:49
neural networks [2.2] : training neural networks - loss function
-
![neural networks [1.5] : feedforward neural network - capacity of neural network](https://i.ytimg.com/vi/O4I7dQC4VtU/mqdefault.jpg)
8:56
neural networks [1.5] : feedforward neural network - capacity of neural network
-
![neural networks [2.4] : training neural networks - hidden layer gradient](https://i.ytimg.com/vi/xFhM_Kwqw48/mqdefault.jpg)
15:14
neural networks [2.4] : training neural networks - hidden layer gradient
-
1:01:32
lecture 02 - basics of neural networks | mit 6.s965
-
1:19:14
lecture 11: training neural networks ii
-
49:01
mit introduction to deep learning (2022) | 6.s191
-
![neural networks [1.1] : feedforward neural network - artificial neuron](https://i.ytimg.com/vi/SGZ6BttHMPw/mqdefault.jpg)
7:51
neural networks [1.1] : feedforward neural network - artificial neuron
-
![neural networks [2.11] : training neural networks - optimization](https://i.ytimg.com/vi/Bver7Ttgb9M/mqdefault.jpg)
23:40
neural networks [2.11] : training neural networks - optimization
-
![neural networks [2.10] : training neural networks - model selection](https://i.ytimg.com/vi/Fs-raHUnF2M/mqdefault.jpg)
13:48
neural networks [2.10] : training neural networks - model selection
-
![neural networks [2.5] : training neural networks - activation function derivative](https://i.ytimg.com/vi/tf9p1xQbWNM/mqdefault.jpg)
4:37
neural networks [2.5] : training neural networks - activation function derivative
-
![neural networks [2.3] : training neural networks - output layer gradient](https://i.ytimg.com/vi/1N837i4s1T8/mqdefault.jpg)
12:03
neural networks [2.3] : training neural networks - output layer gradient
-
![neural networks [1.2] : feedforward neural network - activation function](https://i.ytimg.com/vi/tCHIkgWZLOQ/mqdefault.jpg)
5:56
neural networks [1.2] : feedforward neural network - activation function
-
![neural networks [1.6] : feedforward neural network - biological inspiration](https://i.ytimg.com/vi/cuJ4IC5_pGs/mqdefault.jpg)
14:21
neural networks [1.6] : feedforward neural network - biological inspiration
-
![neural networks [1.4] : feedforward neural network - multilayer neural network](https://i.ytimg.com/vi/apPiZd-qnZ8/mqdefault.jpg)
13:11
neural networks [1.4] : feedforward neural network - multilayer neural network
-
![neural networks [1.3] : feedforward neural network - capacity of single neuron](https://i.ytimg.com/vi/iT5P4z6Fzj8/mqdefault.jpg)
8:04
neural networks [1.3] : feedforward neural network - capacity of single neuron
-
![neural networks [2.9] : training neural networks - parameter initialization](https://i.ytimg.com/vi/sLfogkzFNfc/mqdefault.jpg)
6:10
neural networks [2.9] : training neural networks - parameter initialization
-
![neural networks [2.8] : training neural networks - regularization](https://i.ytimg.com/vi/JfkbyODyujw/mqdefault.jpg)
13:15
neural networks [2.8] : training neural networks - regularization