Practical examples of Concepts of Neural Networks
汇报人：思博
Group 20
11 月24日
神经网络概念的实用示例
Purpose
Try to understand some notions of concepts leveraging graphical tools and interactions.
Content
- 1 neuron and problems that solve
- Activation Function
- 2 groups
- XOR
- 1 layer and problems that solve
- Features
- Train & Test data
- Learning Rate
- Underfiting & Overfiting
- Regularization
- Real Data
Classification Problem (simplification)
1 variable
2 variable
2 categories
Activation function (purpose)
Lineal classification superposition create lineal results
This is the necessity to use "another element" in the architecture
Activation function (3D)
Activation function (3D)
1 Neuron (perceptron)
1 var, 2 vars, XOR
1 Variable
2 Variables
XOR Distribution (no linearly separable)
1 layer
features
More complex example (lineal)
More complex example (+f quadratic)
More complex example (+f trigonometric)
network
Underfitting, Overfitting, learning rate
Learning rate
Learning rate
The ability to deal with overfitting is what separates professionals and amateurs in machine learning.
处理过拟合的能力是区分机器学习专业人员和业余爱好者的关键所在
Underfitting
Underfitting
Overfitting
Regularization L1 & L2
Real Data
Thanks 谢谢
