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Fuzzy Logic

001 right and wrong
002
Set theory
003
Mathematics
004
Membership
005
Calculating with sets
006
Calculating
with sets
007
Small and large people sharp
008
Small
and large people fuzzy
009
Fuzzy membership
010
Fuzzy Complement
011
Fuzzy average (AND)
012
Fuzzy union (OR)
013
laws of set theory
014
The sentence of the excluded third party
015
Strange statements!
016
Truth and falsehood
017
Relations
018
Probability
019
Possibility

Fuzzy control 
020
What is a controler?
021
Shower temperature
022
Fuzzy water temperature
023
Linguistic variables
024
Rule base
025
Output membership functions
026
Complete the rule basis
027
Inference
028
Aggregation
029
Implication
030
Accumulation
031
Centre of gravity method
032
Characteristic curve
033
Linear and nonlinear
034 2 input variables and one output
035
Aggregation and Implication
036
Accumulation
037
Defuzzyfie Singletons
038
3D map
039 Advantages of the fuzzy maps

Neuronal Networks 
040
Introduction
041
The Brain
042
The
brain and its performance
043
The network
044
Neurons
045
The neuron and synapse
046
Learning
047
The artificial elements
048
Function of the artificial elements
049
Example XOR function
050
Example
XOR function
051
Separability
052
How does a human learn?
053
Supervised learning
054
Linear and nonlinear transfer function
055
How do you change the weights?
056
Backpropagation Lernmethode
057
Learning rate
058
Local and global minima
059
Learning without teachers
060
Networks topolgy

Application neural networks 
061
Introduction
062
Identification of a controlled system
063
Example Identification
064
Training and validation data
065
State space and standardization
066
Termination threshold for the learning process
067
Autonomous driving
068
NN learns to drive a truck backwards
069
Data analysis
070
Diagnosis and prognosis
071
Data Mining and Big Data

Machine learning with PyBrain 
072
What does Machine Learning mean?
073
NN is learning
074
Supervised NN in PyBrain
075
Supervised NN in PyBrain
076
Supervised Learning algorithm
077
Backpropagation is active
078
How to use a network?
079
More training is better?
080
Is more training better?
081
More neurons?
082
More neurons?
083
Overloading
084
Increase inputoutput
085
More neurons!
086
Learning rates and weights
087
Momentum!
088
Saving the weights
089
Why normalize to 0 and 1?
090
Learning until a threshold is reached
091
More data
092
Random data between 0 and 1
093
Two inputs and one output
094
From regression to classification
095
Classification with neural networks
096
Classification = the result
097
Sequential data type

NN with TensorFlow

098
TensorFlow
099
Simple example
100
Simple Example Overview
101
MNIST pictures
102
Datasets at TensorFlow
103
Load
pictures
104
Normalizing Images
105
Building a neural network
106
First layer
107
Hidden layer
108
Dropout Layer
109
Output layer
110
NN Block diagram
111
Compile Model
112
Cost function
113
Metrics
114
NN learns
115
NN test
116
NN run in Browser
117
NN run in SPYDER
118
Parallel computer and cloud

Genetic Algorithms 
119
What does optimization mean?
120
Charles Darwin
121
Evolutionary Algorithms
122
Genetic Algorithms
123
Genetic coding in nature
124
Functional Genetic Algorithms
125
Coding of the parameters
126
Population
127
Fitness Function
128
Optimization procedure
129
Selection
130
Modification of genetic information
131
Crossover
132
Mutation
133
Offspring and the next generation

Chaos Theory 
134
Introduction
135
Weather Forecast
136
Prediction and determinism
137
FeigenbaumDiagram
138
The Lorenz water wheel
 THE END

