User:Alexander Roidl/neuralnetsanddeeplearning
Machine Learning
Machine Learning is a term used to describe certain algorithms that »learn«. But learning can be seen as a quite broad term, for some applications it really comes down to a statistical calculation
Terms:
- Generalisation
- Overfitting
- Unterfitting
- Features
K–Nearest Neighbour
K = Variable
Plots Features as Vectors and sorts them in space > Clusters them
The new datapoint is also put in this space and the K nearest points are used to categorise the new point.
Linear models
Draw a linear border between 2 or more classes
Decision Tree
Cain of if / else statements
Neural Networks
Same as Deeplearning
Uses different Layers and a networked structure to find the best values for the wanted result.
Feedforward -> Backpropagation …
It consists of a lot of so called neurons.
There are different kind of neurons:
- Perceptron: outputs 0 or 1
- Sigmoid: outputs a value between 0 and 1: f(x) = 1/1+e^(-x)
- ReLU: same as Sigmoid but cuts values: -1<x>1
So if you take different or multiple of those networks you end up with one of the following popular concepts:
LSTM: Long Short-Term Memory
Stores last results in a neural network itself
Good for text-generation (prevents repeating)
Recurrent Neural Networks
CNN: Convolutional Neural Networks
Especially for images
GAN: Generative Adversarial Network
Uses two neural networks that oppose each other. One generates random data, the other one figures if it is fake or real. So the first one is trained to fool the discriminator.