- Artificial neural networks are inspired by biological neural networks and learning processes. They attempt to mimic the workings of the brain using simple units called artificial neurons that are connected in networks.
- Learning in neural networks involves modifying the synaptic strengths between neurons through mathematical optimization techniques. The goal is to minimize an error function that measures how well the network can approximate or complete a task.
- Neural networks can learn complex nonlinear functions through training algorithms like backpropagation that determine how to adjust the synaptic weights to improve performance on the learning task.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Artificial neural network are the mathematical inventions motivated by observation made in study of biological system, through loosely founded on the actual biology. An artificial neural network can be defined as mapping an input space to output space. This concept is analogous to that of mathematical function. The purpose of neural network is to map an input into desired output. Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted that means that every input value is multiplied with individual weight.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Artificial neural network are the mathematical inventions motivated by observation made in study of biological system, through loosely founded on the actual biology. An artificial neural network can be defined as mapping an input space to output space. This concept is analogous to that of mathematical function. The purpose of neural network is to map an input into desired output. Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted that means that every input value is multiplied with individual weight.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
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0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
3. Artificial Neural Networks
Artificial neural network (ANN) is a
machine learning approach that models
human brain and consists of a number of
artificial neural.
Neural in Anns tend to have fewer
connections than biological neurals.
4. Biological inspiration
Animals are able to react adaptively to changes in their
external and internal environment, and they use their nervous
system to perform these behaviours.
An appropriate model/simulation of the nervous system
should be able to produce similar responses and behaviours in
artificial systems.
The nervous system is build by relatively simple units, the
neurons, so copying their behavior and functionality should be
the solution.
7. Biological inspiration
The spikes travelling along the axon of the pre-synaptic
neuron trigger the release of neurotransmitter substances
at the synapse.
The neurotransmitters cause excitation or inhibition in the
dendrite of the post-synaptic neuron.
The integration of the excitatory and inhibitory signals
may produce spikes in the post-synaptic neuron.
The contribution of the signals depends on the strength of
the synaptic connection.
8. Artificial neurons
Neurons work by processing information. They receive and
provide information in form of spikes.
The McCullogh-Pitts model
Inputs
Output
w2
w1
w3
wn
wn-1
.
.
.
x1
x2
x3
…
xn-1
xn
y
)(;
1
zHyxwz
n
i
ii == ∑=
9. Artificial neurons
The McCullogh-Pitts model:
• spikes are interpreted as spike rates;
• synaptic strength are translated as synaptic weights;
• excitation means positive product between the
incoming spike rate and the corresponding synaptic
weight;
• inhibition means negative product between the
incoming spike rate and the corresponding synaptic
weight;
10. Artificial neurons
Nonlinear generalization of the McCullogh-Pitts
neuron:
),( wxfy =
y is the neuron’s output, x is the vector of inputs, and w
is the vector of synaptic weights.
Examples:
2
2
2
||||
1
1
a
wx
axw
ey
e
y T
−
−
−−
=
+
= sigmoidal neuron
Gaussian neuron
11. Artificial neural networksInputs
Output
An artificial neural network is composed of many artificial
neurons that are linked together according to a specific
network architecture. The objective of the neural network
is to transform the inputs into meaningful outputs.
12. Artificial neural networks
Tasks to be solved by artificial neural networks:
• controlling the movements of a robot based on self-
perception and other information (e.g., visual
information);
• deciding the category of potential food items (e.g.,
edible or non-edible) in an artificial world;
• recognizing a visual object (e.g., a familiar face);
• predicting where a moving object goes, when a robot
wants to catch it.
13. Learning in biological systems
Learning = learning by adaptation
The young animal learns that the green fruits are sour,
while the yellowish/reddish ones are sweet. The learning
happens by adapting the fruit picking behavior.
At the neural level the learning happens by changing of the
synaptic strengths, eliminating some synapses, and
building new ones.
14. Learning as optimisation
The objective of adapting the responses on the basis of the
information received from the environment is to achieve a
better state. E.g., the animal likes to eat many energy rich,
juicy fruits that make its stomach full, and makes it feel
happy.
In other words, the objective of learning in biological
organisms is to optimise the amount of available resources,
happiness, or in general to achieve a closer to optimal state.
15. Learning in biological neural
networks
The learning rules of Hebb:
• synchronous activation increases the synaptic strength;
• asynchronous activation decreases the synaptic
strength.
These rules fit with energy minimization principles.
Maintaining synaptic strength needs energy, it should be
maintained at those places where it is needed, and it
shouldn’t be maintained at places where it’s not needed.
16. Learning principle for
artificial neural networks
ENERGY MINIMIZATION
We need an appropriate definition of energy for artificial
neural networks, and having that we can use
mathematical optimisation techniques to find how to
change the weights of the synaptic connections between
neurons.
ENERGY = measure of task performance error
19. MLP neural networks
MLP = multi-layer perceptron
Perceptron:
MLP neural network:
xwy T
out = x yout
x yout
23
2
1
23
2
2
2
1
2
2
1
3
1
2
1
1
1
1
),(
2,1,
1
1
),,(
3,2,1,
1
1
212
11
ywywy
yyy
k
e
y
yyyy
k
e
y
T
k
kkout
T
aywk
T
axwk
k
kT
k
kT
==
=
=
+
=
=
=
+
=
∑
=
−−
−−
20. RBF neural networks
RBF = radial basis function
2
2
2
||||
)(
||)(||)(
a
wx
exf
cxrxr
−
−
=
−=
Example: Gaussian RBF
x
yout
∑
=
−
−
⋅=
4
1
)(2
||||
2 2
2,1
k
a
wx
kout
k
k
ewy
21. Neural network tasks
• control
• classification
• prediction
• approximation
These can be reformulated
in general as
FUNCTION
APPROXIMATION
tasks.
Approximation: given a set of values of a function g(x)
build a neural network that approximates the g(x) values
for any input x.
22. Neural network approximation
Task specification:
Data: set of value pairs: (xt
, yt), yt=g(xt
) + zt; zt is random
measurement noise.
Objective: find a neural network that represents the input /
output transformation (a function) F(x,W) such that
F(x,W) approximates g(x) for every x
23. Learning to approximate
Error measure:
∑
=
−=
N
t
tt yWxF
N
E
1
2
));((
1
Rule for changing the synaptic weights:
j
i
j
i
newj
i
j
i
j
i
www
W
w
E
cw
∆+=
∂
∂
⋅−=∆
,
)(
c is the learning parameter (usually a constant)
24. Learning with a perceptron
Perceptron: xwy T
out =
Data: ),(),...,,(),,( 2
2
1
1
N
N
yxyxyx
Error:
22
))(())(()( t
tT
tout yxtwytytE −=−=
Learning:
t
j
m
j
j
T
t
it
tT
ii
i
t
tT
i
i
ii
xtwxtw
xyxtwctwtw
w
yxtw
ctw
w
tE
ctwtw
⋅=
⋅−⋅−=+
∂
−∂
⋅−=
∂
∂
⋅−=+
∑
=1
2
)()(
))(()()1(
))((
)(
)(
)()1(
A perceptron is able to learn a linear function.
25. Learning with RBF neural
networks
RBF neural network:
Data: ),(),...,,(),,( 2
2
1
1
N
N
yxyxyx
Error: 2
1
)(2
||||
22
))(())(()(
2
2,1
t
M
k
a
wx
ktout yetwytytE k
kt
−⋅=−= ∑
=
−
−
Learning:
2
2,1
)(2
||||
2
2
22
)))(,((2
)(
)(
)()1(
i
it
a
wx
t
t
i
i
ii
eytWxF
w
tE
w
tE
ctwtw
−
−
⋅−⋅=
∂
∂
∂
∂
⋅−=+
Only the synaptic weights of the output neuron are modified.
An RBF neural network learns a nonlinear function.
∑
=
−
−
⋅==
M
k
a
wx
kout
k
k
ewWxFy
1
)(2
||||
2 2
2,1
),(
26. Learning with MLP neural
networks
MLP neural network:
with p layers
Data: ),(),...,,(),,( 2
2
1
1
N
N
yxyxyx
Error: 22
));(())(()( t
t
tout yWxFytytE −=−=
1
22
1
2
2
2
11
1
1
1
1
);(
...
),...,(
,...,1,
1
1
),...,(
,...,1,
1
1
2
212
1
11
−
−−
−−
==
=
=
+
=
=
=
+
=
ppT
out
T
M
aywk
T
M
axwk
ywWxFy
yyy
Mk
e
y
yyy
Mk
e
y
k
kT
k
kT
It is very complicated to calculate the weight changes.
x
yout
1 2 … p-1 p
27. Learning with backpropagation
Solution of the complicated learning:
• calculate first the changes for the synaptic weights
of the output neuron;
• calculate the changes backward starting from layer
p-1, and propagate backward the local error terms.
The method is still relatively complicated but it
is much simpler than the original optimisation
problem.
28. Learning with general optimisation
In general it is enough to have a single layer of nonlinear
neurons in a neural network in order to learn to
approximate a nonlinear function.
In such case general optimisation may be applied without
too much difficulty.
Example: an MLP neural network with a single hidden layer:
∑
=
−−
+
⋅==
M
k
axwkout
k
kT
e
wWxFy
1
2
,1
1
1
);(
29. Learning with general optimisation
i
tiT
axwt
t
i
i
ii
e
ytWxF
w
tE
w
tE
ctwtw
−−
+
⋅−⋅=
∂
∂
∂
∂
⋅−=+
,1
1
1
)))(,((2
)(
)(
)()1(
2
2
22
Synaptic weight change rules for the output neuron:
Synaptic weight change rules for the neurons of the
hidden layer:
( )
( )
( )
( )
)(
1
)))(,((2)()1(
11
1
1
1
)))(,((2
)(
)(
)()1(
2
,1,1
,1
,1
,1
,12,1
,1,1
,1
,1,1
,1
,1
,1
,1
,1
,1
t
j
axw
axw
t
ti
j
i
j
t
ji
tiT
i
j
i
tiT
i
j
axw
axw
axwi
j
axwi
j
t
t
i
j
i
j
i
j
i
j
x
e
e
ytWxFctwtw
xaxw
w
axw
we
e
ew
ew
ytWxF
w
tE
w
tE
ctwtw
i
tiT
i
tiT
i
tiT
i
tiT
i
tiT
i
tiT
−⋅
+
⋅−⋅⋅−=+
−=−−
∂
∂
−−
∂
∂
⋅
+
=
+∂
∂
+∂
∂
⋅−⋅=
∂
∂
∂
∂
⋅−=+
−−
−−
−−
−−
−−
−−
30. New methods for learning with
neural networks
Bayesian learning:
the distribution of the neural network
parameters is learnt
Support vector learning:
the minimal representative subset of the
available data is used to calculate the synaptic
weights of the neurons
31. Summary
• Artificial neural networks are inspired by the learning
processes that take place in biological systems.
• Artificial neurons and neural networks try to imitate the
working mechanisms of their biological counterparts.
• Learning can be perceived as an optimisation process.
• Biological neural learning happens by the modification
of the synaptic strength. Artificial neural networks learn
in the same way.
• The synapse strength modification rules for artificial
neural networks can be derived by applying
mathematical optimisation methods.
32. Summary
• Learning tasks of artificial neural networks can be
reformulated as function approximation tasks.
• Neural networks can be considered as nonlinear function
approximating tools (i.e., linear combinations of nonlinear
basis functions), where the parameters of the networks
should be found by applying optimisation methods.
• The optimisation is done with respect to the approximation
error measure.
• In general it is enough to have a single hidden layer neural
network (MLP, RBF or other) to learn the approximation of
a nonlinear function. In such cases general optimisation can
be applied to find the change rules for the synaptic weights.