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Artificial Intelligence
Data
analysis
Expert
Systems
Copyright
 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 
