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首页Jim Liang Get started with machine learning 学习笔记下（311页到520页）

Jim Liang Get started with machine learning 学习笔记下（311页到520页）

机器学习

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Jim Liang Get started with machine learning 学习笔记（311页到520页） Jim Liang Get started with machine learning 学习笔记（311页到520页）

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Created by: Jim Liang

Neural Network

Created by: Jim Liang

:: Neural Network

Structure of Neurons in human brain

The human brain can be described as a biological neural network—an interconnected web of neurons transmitting elaborate patterns of electrical signals. Dendrites

receive input signals and, based on those inputs, fire an output signal via an axon

1 http://natureofcode.com/book/chapter-10-neural-networks/

Dendrite• : It receives signals from other neurons

• Cell body: It sums all the incoming signals to generate input

Axon• : When the sum reaches a threshold value, neuron ﬁres and the signal travels

down the axon to the other neurons

Created by: Jim Liang

:: Neural Network

The neuron is the basic computational unit of the brain

Neuron , often called a node or unit, receives input from some other neurons, or from an external source and computes an output. Each input has an associated

weight (w), which is assigned on the basis of its relative importance to other inputs. The node applies a function to the weighted sum of its inputs.

The idea is that the synaptic strengths (the weights w) are learnable and control the strength of influence and its direction: excitory (positive weight) or inhibitory

(negative weight) of one neuron on another. In the basic model, the dendrites carry the signal to the cell body where they all get summed. If the final sum is above

a certain threshold, the neuron can fire, sending a spike along its axon

Artificial neural networks are computing systems inspired by the biological neural networks.

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biological neuron (left) and a common mathematical model (right)

(

is input,

is the weight)

Created by: Jim Liang

:: Neural Network

Artiﬁcial neural network (ANN)

Inspired by the human brain, an ANN is comprised of a network of artiﬁcial neurons (also known as "nodes"). These nodes are connected to each other, and the strength of

their connections to one another is assigned a value based on their strength. If the value of the connection is high, then it indicates that there is a strong connection

. The

network is trained by iteratively modifying the strengths of the connections so that given inputs map to the correct response

1 Source: http://www.saedsayad.com/artificial_neural_network.htm

2Source : Mathworks, Applying Supervised Learning

All these connections have weights associated with them.