Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
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30. Activation Functions
Which activation function to use?
Outputs
Class
Labels
Activation
Function
TWO Class
Labels
TWO
Outputs
One that gives two outputs.
Which activation function to use?
𝑪𝒋𝒀𝒋
BA
01
1
10
00
0
11
BA
01
1 10
00
0 11
67. Neural Networks Training Steps
Weights Initialization
Inputs Application
Sum of Inputs-Weights Products
Activation Function Response Calculation
Weights Adaptation
Back to Step 2
1
2
3
4
5
6
68. Regarding 5th Step: Weights Adaptation
• If the predicted output Y is not the same as the desired output d,
then weights are to be adapted according to the following equation:
𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + η 𝒅 𝒏 − 𝒀 𝒏 𝑿(𝒏)
Where
𝑾 𝒏 = [𝒃 𝒏 , 𝑾 𝟏(𝒏), 𝑾 𝟐(𝒏), 𝑾 𝟑(𝒏), … , 𝑾 𝒎(𝒏)]
69. Neural Networks
Training Example
Step n=0
• In each step in the solution, the parameters of the neural network
must be known.
• Parameters of step n=0:
η = .001
𝑋 𝑛 = 𝑋 0 = +1, +1, +1,1, 0
𝑊 𝑛 = 𝑊 0 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6
= −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1
𝑑 𝑛 = 𝑑 0 = 1
BA
01
1 => 1
10
00
0 => 0
11