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Artificial neural networks

An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.

Artificial neural networks

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Artificial neural networks
 INTRODUCTION
 HISTORY
 BIOLOGICAL NEURON MODEL
 ARTIFICIAL NEURON MODEL
 ARTIFICIAL NEURAL NETWORK
 NEURAL NETWORK ARCHITECTURE
 LEARNING
 BACKPROPAGATION ALGORITHM
 APPLICATIONS
 ADVANTAGES
 CONCLUSION
 “Neural“ is an adjective for neuron, and “network” denotes a
graph like structure.
 Artificial Neural Networks are also referred to as “neural
nets” , “artificial neural systems”, “parallel distributed
processing systems”, “connectionist systems”.
 For a computing systems to be called by these pretty names, it
is necessary for the system to have a labeled directed graph
structure where nodes performs some simple computations.
 “Directed Graph” consists of set of “nodes”(vertices) and a set
of “connections”(edges/links/arcs) connecting pair of nodes.
 A graph is said to be “labeled graph” if each connection is
associated with a label to identify some property of the
connection
Fig 1: AND gate graph
This graph cannot be considered a neural
network since the connections between the
nodes are fixed and appear to play no other
role than carrying the inputs to the node
that computed their conjunction.
Fig 2: AND gate network
The graph structure which connects the
weights modifiable using a learning
algorithm, qualifies the computing
system to be called an artificial neural
networks.
x2ϵ{0,1}
x1 x2
x1ϵ{0,1}
o = x1 AND x2
multiplier
(x1 w1)
(x2w2)
o = x1 AND x2
x1
x2
w1
w2
• The field of neural network was pioneered by BERNARD WIDROW of Stanford
University in 1950’s.
CONTD…
 late-1800's - Neural Networks appear as an analogy to biological
systems
 1960's and 70's – Simple neural networks appear
 Fall out of favor because the perceptron is not effective by itself, and
there were no good algorithms for multilayer nets
 1986 – Backpropagation algorithm appears
 Neural Networks have a resurgence in popularity
 Records (examples) need to be represented as a (possibly large)
set of tuples of <attribute, value>
 The output values can be represented as a discrete value, a real
value, or a vector of values
 Tolerant to noise in input data
 Time factor
 It takes long time for training
 Once trained, an ANN produces output values (predictions) fast
 It is hard for human to interpret the process of prediction by
ANN

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Artificial neural networks

  • 2.  INTRODUCTION  HISTORY  BIOLOGICAL NEURON MODEL  ARTIFICIAL NEURON MODEL  ARTIFICIAL NEURAL NETWORK  NEURAL NETWORK ARCHITECTURE  LEARNING  BACKPROPAGATION ALGORITHM  APPLICATIONS  ADVANTAGES  CONCLUSION
  • 3.  “Neural“ is an adjective for neuron, and “network” denotes a graph like structure.  Artificial Neural Networks are also referred to as “neural nets” , “artificial neural systems”, “parallel distributed processing systems”, “connectionist systems”.  For a computing systems to be called by these pretty names, it is necessary for the system to have a labeled directed graph structure where nodes performs some simple computations.  “Directed Graph” consists of set of “nodes”(vertices) and a set of “connections”(edges/links/arcs) connecting pair of nodes.  A graph is said to be “labeled graph” if each connection is associated with a label to identify some property of the connection
  • 4. Fig 1: AND gate graph This graph cannot be considered a neural network since the connections between the nodes are fixed and appear to play no other role than carrying the inputs to the node that computed their conjunction. Fig 2: AND gate network The graph structure which connects the weights modifiable using a learning algorithm, qualifies the computing system to be called an artificial neural networks. x2ϵ{0,1} x1 x2 x1ϵ{0,1} o = x1 AND x2 multiplier (x1 w1) (x2w2) o = x1 AND x2 x1 x2 w1 w2 • The field of neural network was pioneered by BERNARD WIDROW of Stanford University in 1950’s. CONTD…
  • 5.  late-1800's - Neural Networks appear as an analogy to biological systems  1960's and 70's – Simple neural networks appear  Fall out of favor because the perceptron is not effective by itself, and there were no good algorithms for multilayer nets  1986 – Backpropagation algorithm appears  Neural Networks have a resurgence in popularity
  • 6.  Records (examples) need to be represented as a (possibly large) set of tuples of <attribute, value>  The output values can be represented as a discrete value, a real value, or a vector of values  Tolerant to noise in input data  Time factor  It takes long time for training  Once trained, an ANN produces output values (predictions) fast  It is hard for human to interpret the process of prediction by ANN
  • 7. 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 contact between the neurons.
  • 8.  Inputs to the network are represented by the mathematical symbol, xn  Each of these inputs are multiplied by a connection weight , wn sum = w1 x1 + ……+ wnxn  These products are simply summed, fed through the transfer function, f( ) to generate a result and then output. f w1 w2 xn x2 x1 wn f(w1 x1 + ……+ wnxn)
  • 9. Biological Terminology Artificial Neural Network Terminology Neuron Node/Unit/Cell/Neurode Synapse Connection/Edge/Link Synaptic Efficiency Connection Strength/Weight Firing frequency Node output
  • 10.  Artificial Neural Network (ANNs) are programs designed to solve any problem by trying to mimic the structure and the function of our nervous system.  Neural networks are based on simulated neurons, Which are joined together in a variety of ways to form networks.  Neural network resembles the human brain in the following two ways: - * A neural network acquires knowledge through learning. *A neural network’s knowledge is stored within the interconnection strengths known as synaptic weight.
  • 11. output layer connections Input layer Hidden layers Neural network Including connections (called weights) between neuron Comp are Actual output Desired output Input output Figure showing adjust of neural network Fig 1 : artificial neural network model CONTD…
  • 12. The neural network in which every node is connected to every other nodes, and these connections may be either excitatory (positive weights), inhibitory (negative weights), or irrelevant (almost zero weights). These are networks in which nodes are partitioned into subsets called layers, with no connections from layer j to k if j > k. Input node Input node output node output node Hidden node Layer 1 Layer2 Layer0 Layer3 (Input layer) (Output layer) Hidden Layer Fig: fully connected network fig: layered network
  • 13. This is the subclass of the layered networks in which there is no intra- layer connections. In other words, a connection may exist between any node in layer i and any node in layer j for i < j, but a connection is not allowed for i=j. fig : Feedforward network This is a subclass of acyclic networks in which a connection is allowed from a node in layer i only to nodes in layer i+1 Layer 1 Layer2 Layer0 Layer3 (Input layer) (Output layer) Hidden Layer Layer 1 Layer2 Layer0 Layer3 (Input layer) (Output layer) Hidden Layer Fig : Acyclic network CONTD…
  • 14. Many problems are best solved using neural networks whose architecture consists of several modules, with sparse interconnections between them. Modules can be organized in several different ways as Hierarchial organization, Successive refinement, Input modularity Fig : Modular neural network CONTD…
  • 15.  Neurons in an animal’s brain are “hard wired”. It is equally obvious that animals, especially higher order animals, learn as they grow.  How does this learning occur?  What are possible mathematical models of learning?  In artificial neural networks, learning refers to the method of modifying the weights of connections between the nodes of a specified network.  The learning ability of a neural network is determined by its architecture and by the algorithmic method chosen for training.
  • 16. UNSUPERVISED LEARNING  This is learning by doing.  In this approach no sample outputs are provided to the network against which it can measure its predictive performance for a given vector of inputs.  One common form of unsupervised learning is clustering where we try to categorize data in different clusters by their similarity. • A teacher is available to indicate whether a system is performing correctly, or to indicate the amount of error in system performance. Here a teacher is a set of training data. • The training data consist of pairs of input and desired output values that are traditionally represented in data vectors. • Supervised learning can also be referred as classification, where we have a wide range of classifiers, (Multilayer perceptron, k nearest neighbor..etc) SUPERVISED LEARNING CONTD…
  • 17.  The backpropagation algorithm (Rumelhart and McClelland, 1986) is used in layered feed-forward Artificial Neural Networks.  Back propagation is a multi-layer feed forward, supervised learning network based on gradient descent learning rule.  we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error (difference between actual and expected results) is calculated.  The idea of the backpropagation algorithm is to reduce this error, until the Artificial Neural Network learns the training data.
  • 18.  The activation function of the artificial neurons in ANNs implementing the backpropagation algorithm is a weighted sum (the sum of the inputs xi multiplied by their respective weights wji)  The most common output function is the sigmoidal function:  Since the error is the difference between the actual and the desired output, the error depends on the weights, and we need to adjust the weights in order to minimize the error. We can define the error function for the output of each neuron: Inputs, x Weights, v weights, w output Fig: Basic Block of Back propagation neural network
  • 19.  The backpropagation algorithm now calculates how the error depends on the output, inputs, and weights. the adjustment of each weight (Δwji ) will be the negative of a constant eta (η) multiplied by the dependance of the “wji” previous weight on the error of the network.  First, we need to calculate how much the error depends on the output  Next, how much the output depends on the activation, which in turn depends on the weights  And so, the adjustment to each weight will be CONTD…
  • 20.  If we want to adjust vik, the weights (let’s call them vik ) of a previous layer, we need first to calculate how the error depends not on the weight, but in the input from the previous layer i.e. replacing w by x as shown in below equation. where  and Inputs, x Weights, v weights, w output CONTD…
  • 21.  Neural Networks in Practice  Neural networks in medicine • Modelling and Diagnosing the Cardiovascular System • Electronic noses • Instant Physician  Neural Networks in business  Marketing  Credit Evaluation
  • 22.  It involves human like thinking.  They handle noisy or missing data.  They can work with large number of variables or parameters.  They provide general solutions with good predictive accuracy.  System has got property of continuous learning.  They deal with the non-linearity in the world in which we live.
  • 23. • 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.
  • 24. • 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.