Learning Algorithm and Neural Networks

1. Dr. Alaa Sagheer alaa@ieee.org Learning Algorithm and Neural Networks MTR607- Spring 2012 Egypt-Japan University

2. MTR 607 • Textbook:Simon Haykin, “Neural Networks A Comprehensive Foundation,” 2nd Ed., 1999 • Lecturer:Dr. Alaa Sagheer • Place:Seminar Room, E-JUST • Grading: Class participation (10%), Assignments and reports (20%), Midterm test (30%), Final exam (40%) MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

3. Course Overview Introduction to Artificial Neural Networks, Artificial and human neurons (Biological Inspiration) The learning process, Supervised and unsupervised learning, Reinforcement learning, Applications Development and Portfolio The McCulloch-Pitts Model of Neuron, A simple network layers, Multilayer networks Perceptron, Back propagation algorithm, Recurrent networks, Associative memory, Self Organizing maps, Support Vector Machine and PCA, Applications to speech, vision and control problems. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

4. ANN’s Resources • Main text books: • “Neural Networks: A Comprehensive Foundation”, S. Haykin (very good -theoretical) • “Pattern Recognition with Neural Networks”, C. Bishop (very good-more accessible) • “Neural Network Design” by Hagan, Demuth and Beale (introductory) • Books emphasizing the practical aspects: • “Neural Smithing”, Reeds and Marks • “Practical NeuralNetwork Recipees in C++”’ T. Masters • Seminal Paper: • “Parallel Distributed Processing” Rumelhart and McClelland et al. • Other: • “Neural and Adaptive Systems”, J. Principe, N. Euliano, C. Lefebvre MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

5. ANN’s Resources • Review Articles: • R. P. Lippman, “An introduction to Computing with Neural Nets”’ IEEE ASP Magazine, 4-22, April 1987. • T. Kohonen, “An Introduction to Neural Computing”, Neural Networks, 1, 3-16, 1988. • A. K. Jain, J. Mao, K. Mohuiddin, “Artificial Neural Networks: A Tutorial”’ IEEE Computer, March 1996’ p. 31-44. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

6. Course Overview Introduction to Artificial Neural Networks, Artificial and human neurons (Biological Inspiration) The learning process, Supervised and unsupervised learning, Reinforcement learning, Applications Development and Portfolio The McCulloch-Pitts Model of Neuron, A simple network layers, Multilayer networks Perceptron, Back propagation algorithm, Recurrent networks, Associative memory, Self Organizing maps, Support Vector Machine and PCA, Applications to speech, vision and control problems. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

7. Introduction to Artificial Neural Networks • Part I: • Artificial Neural Networks • Artificial and human neurons (Biological Inspiration) • Tasks & Applications of ANNs • Part II: • Learning in Biological Systems • Learning with Artificial Neural Networks MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

8. Digital Computers Analyze the problem to be solved Deductive Reasoning. We apply known rules to input data to produce output. Computation is centralized, synchronous, and serial. Not fault tolerant. One transistor goes and it no longer works. Static connectivity. Applicable if well defined rules with precise input data. Artificial Neural Networks No requirements of an explicit description of the problem. Inductive Reasoning. Given input and output data (training examples), we construct the rules. Computation is collective, asynchronous, and parallel. Fault tolerant and sharing of responsibilities. Dynamic connectivity. Applicable if rules are unknown or complicated, or if data are noisy or partial. ANNs vs. Computers MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

9. Artificial Neural Networks (1) What is ANN? • ANN is a branch of "Artificial Intelligence". It is a system modeled based on the human brain. ANN goes by many names, such as connectionism, parallel distributed processing, neuro-computing, machine learning algorithms, and finally, artificial neural networks. • Developing ANNs date back to the early 1940s. It experienced a wide popularity in the late 1980s. This was a result of the discovery of new techniques and developments in PCs. • Some ANNs are models of biological neural networks and some are not. • ANN is a processing device (An algorithm or Actual hardware) whose design was motivated by the design and functioning of human brain. Inside ANN: • ANN’s design is what distinguishes neural networks from other mathematical techniques • ANN is a network of many simple processors ("units“ or “neurons”), each unit has a small amount of local memory. • The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. • The units operate only on their local data and on the inputs they receive via the connections. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

10. Artificial Neural Networks (2) ANNs Operation • ANNs normally have great potential for parallelism (multiprocessor-friendly architecture), since the computations of the units are independent of each other. Same like biological neural networks.  • Most neural networks have some kind of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. • In other words, neural networks "learn" from examples, just like children…and exhibit some structural capability for generalization. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

11. Artificial Neural Networks (3) ANNs are a powerful technique (Black Box) to solve many real world problems. They have the ability to learn from experience in order to improve their performance and to adapt themselves to changes in the environment. In addition, they are able to deal with incomplete information or noisy data and can be very effective especially in situations where it is not possible to define the rules or steps that lead to the solution of a problem. Once trained, the ANN is able to recognize similarities when presented with a new input pattern, resulting in a predicted output pattern. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

12. What can a ANN do? • Compute a known function • Approximate an unknown function • Pattern Recognition • Signal Processing • ……. • Learn to do any of the above

13. Introduction to Artificial Neural Networks • Part I: • Artificial Neural Networks (ANNs) • Artificial and human neurons (Biological Inspiration) • Tasks & Applications of ANNs • Part II: • Learning in Biological Systems • Learning with Artificial Neural Networks MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

14. Biological Inspiration Biological Neural Networks (BNN) are much more complicated in their elementary structures than the mathematical models we use for ANNs • 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 behaviour and functionality should be the solution! MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

15. ANN as a Brain-Like Computer ANN as a model of brain-like Computer • An artificial neural network (ANN) is • a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use. It means that: • Knowledge is acquired by the network • through a learning (training) process; • The strength of the interconnections • between neurons is implemented by • means of the synaptic weights used to • store the knowledge. • The learning process is a procedure of the adapting the weights with a learning algorithm in order to capture the knowledge. On more mathematically, the aim of the learning process is to map a given relation between inputs and output of the network. • Brain • The human brain is still not well understood and indeed its behavior is very complex! • There are about 10-11 billion neurons in the human cortex each connected to , on average, 10000 others. In total 60 trillion synapses of connections. • The brain is a highly complex, nonlinear and parallel computer (information-processing system) MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

16. Massive parallelism Brain computer as an information or signal processing system, is composed of a large number of a simple processing elements, called neurons. These neurons are interconnected by numerous direct links, which are called connection, and cooperate which other to perform a parallel distributed processing (PDP) in order to soft a desired computation tasks. Connectionism Brain computer is a highly interconnected neurons system in such a way that the state of one neuron affects the potential of the large number of other neurons which are connected according to weights or strength. The key idea of such principle isthe functional capacity of biological neural nets deters mostly not so of a single neuron but of its connections Associative distributed memory Storage of information in a brain is supposed to be concentrated in synaptic connections of brain neural network, or more precisely, in the pattern of these connections and strengths (weights) of the synaptic connections. Principles of Brain Processing How our brain manipulates with patterns ? A process of pattern recognition and pattern manipulation is based on: MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

17. Biological Neuron Biological Neuron - The simple “arithmetic computing” element MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

18. Biological Neuron (2) • Cell structures • Cell body • Dendrites • Axon • Synaptic terminals MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

19. Biological Neurons (3) dendrites axon synapses • The information transmission happens at the synapses, i.e • Synaptic connection strengths among neurons are used to store the acquired knowledge. • In a biological system, learning involves adjustments to the synaptic connections between neurons MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

20. Synapses Axon from other neuron Soma Dendrite from other Axon Dendrites The schematic model of a biological neuron Biological Neurons (4) • Soma or body cell - is a large, round central body in which almost all the logical functions of the neuron are realized (i.e. the processing unit). • The axon (output), is a nerve fibre attached to the soma which can serve as a final output channel of the neuron. An axon is usually highly branched. • The dendrites (inputs)- represent a highly branching tree of fibers. These long irregularly shaped nerve fibers (processes) are attached to the soma carrying electrical signals to the cell • Synapses are the point of contact between the axon of one cell and the dendrite of another, regulating a chemical connection whose strength affects the input to the cell. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

21. Properties of ANNs • Learning from examples • labeled or unlabeled • Adaptivity • changing the connection strengths to learn things • Non-linearity • the non-linear activation functions are essential • Fault tolerance • if one of the neurons or connections is damaged, the whole network still works quite well

22. Introduction to Artificial Neural Networks • Part I: • Artificial Neural Networks (ANNs) • Artificial and human neurons (Biological Inspiration) • Tasks & Applications of ANNs • Part II: • Learning in Biological Systems • Learning with Artificial Neural Networks MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

23. Applications of ANNs • Classification In marketing: consumer spending pattern classification In defence: radar and sonar image classification In agriculture & fishing: fruit, fish and catch grading In medicine: ultrasound and electrocardiogram image classification, EEGs, medical diagnosis • Recognition and Identification In general computing and telecommunications: speech, vision andhandwriting recognition In finance: signature verification and bank note verification • Assessment In engineering: product inspection monitoring and control In defence: target tracking In security: motion detection, surveillance image analysis and fingerprint matching • Forecasting and Prediction In finance: foreign exchange rate and stock market forecasting In agriculture: crop yield forecasting , Deciding the category of potential food items (e.g., edible or non-edible) In marketing: sales forecasting In meteorology: weather prediction MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

24. Who are the Men of ANNs?! • Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general. • Statisticians use neural nets as flexible, nonlinear regression and classification models. • Engineers of many kinds exploit the capabilities of neural networks in many areas, such as signal processing and automatic control. • Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and consciousness (High-level brain function). • Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics). • Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks. • Biologists use Neural Networks to interpret nucleotide sequences. • Philosophers and some other people may also be interested in Neural Networks for various reasons MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

25. Operation of Biological Neuron • 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. • 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; MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

26. ANN Architecture Inputs 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. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

27. ANN Architecture (2) Neurons are arranged in layers. Neurons work by processing information. They receive and provide information in form of spikes. The artificial neuron receives one or more inputs (representing the one or more dendrites), At each neuron, every input has an associated weight which modifies the strength of each input and sums them together, The sum of each neuron is passed through a function known as an activation function or transfer function in order to produce an output (representing a biological neuron's axon) MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer Inputs Output

28. ANN Architecture (3) x1 x2 x3 … xn-1 xn w1 Output w2 Inputs y w3 . . . wn-1 wn Each neuron takes one or more inputs and produces an output. At each neuron, every input has an associated weight which modifies the strength of each input. The neuron simply adds together all the inputs and calculates an output to be passed on. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

29. Models of A Neuron MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

30. Models of A Neuron (2) MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

31. Models of A Neuron (3) Three elements: A set of synapses, or connection link: each of which is characterized by a weight or strength of its own wkj. Specifically, a signal xj at the input synapse ‘j’ connected to neuron ‘k’ is multiplied by the synaptic wkj An adder:For summing the input signals, weighted by respective synaptic strengths of the neuron in a linear operation. Activation function: For limiting of the amplitude of the output of the neuron to limited range. The activation function is referred to as a Squashing (i.e. limiting) function {interval [0,1], or, alternatively [-1,1]} MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

32. Bias The bias has the effect of increasing or lowering the net input of the activation function depending on whether it is +/- yk= Ø(vk) = Ø(uk+bk) = Ø(S wkjxj+bk) • An artificial neuron: • computes the weighted sum of its input (called its net input) • adds its bias (the effect of applying affine transformation to the output vk) • passes this value through an activation function • We say that the neuron “fires” (i.e. becomes active) if its outputs is above zero. • This extra free variable (bias) makes the neuron more powerful. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

33. Activation Function Ø(vk) • It defines the output of the neuron given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input, • The best activation function is the non-linear function. Linear functions are limited because the output is simply proportional to the input. Three basic types of activation function: 1. Threshold function, 2. Linear function, 3. Sigmoid function. MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

34. Activation functions (2) Threshold (Step) functionThe output yk of this activation function is binary, depending on whether the input meets a specified threshold. The "signal" is sent, i.e. the output is set to one, if the activation meets the threshold. McColloch-Pitts Model Threshold Logic Unit (TLU), since 1943 MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

35. Activation functions (3) Piecewise Linear Function- The amplification factor inside the linear region of operation is assumed to be unity. - This form may be viewed as an approximation to a non linear amplifier MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

36. Activation functions (4) Sigmoid function • - A fairly simple non-linear function, such as the logistic function. • As the slop parameter approaches infinity the sigmoid function becomes a threshold function Where “a” is the slope parameter of the sigmoid function MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

37. Artificial Neural Networks • Early ANN Models: • McCulloch-Pitts , Perceptron, ADALINE, Hopfield Network, • Current Models: • Multilayer feed forward networks (Multilayer perceptrons- Back propagation ) • Radial Basis Function networks • Self Organizing Networks • ... MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer

38. Feedback Feedback is a dynamic system whenever occurs in almost every part of the nervous system, Feedback is giving one or more closed path for transmission of signals around the system, It plays important role in study of special class of neural networks known as Recurrent networks.

39. Feedback (2) The system is assumed to be linear and has a forward path (A) and a feedback path (B), The output of the forward channel determines its own output through the feedback channel.

40. Feedback (3) E.g. consider A is a fixed weight and B is a unit delay operator z-1 .

41. Feedback (4) Then, we may express yk(n) as an infinite weighted summation of present and past samples of the input signal xj(n). Therefore, feedback systems are controlled by weight.

42. Feedback (5) Feedback systems are controlled by weight. For positive weight, we have stable systems, i,e, convergent output y, For negative weight, we have, unstable systems, i.e divergent output y.. (Linear and Exponential)

43. Network Architectures Three different classes of network architectures: 1. Single-layer feed forward networks, 2. Multilayer feed forward networks, 3. Recurrent networks.

44. Single-layer feed forward network - Input layer of source nodes that projects directly onto an output layer of neurons. - “Single-layer” referring to the output layer of computation nodes (neuron).

45. Multilayer feed forward network It contains one or more hidden layers (hidden neurons). “Hidden” refers to the part of the neural network is not seen directly from either input or output of the network . The function of hidden neuron is to intervene between input and output. By adding one or more hidden layers, the network is able to extract higher-order statistics from input

46. Recurrent Networks It is different from feed forward neural network in that it has at least one feedback loop. Recurrent network may consist of single layer of neuron with each neuron feeding its output signal back to the inputs of all the other neurons. Note: There are no self-feedback. Feedback loops have a profound impact on learning and overall performance.

47. How to Decide on a Network Topology? • What transfer function should be used? • How many inputs does the network need? • How many hidden layers does the network need? • How many hidden neurons per hidden layer? • How many outputs should the network have? There is no standard methodology to determinate these values. Even there is some heuristic points, final values are determinate by a trial and error procedure.

48. Knowledge Representation Knowledgeis referred to the stored information or models used by a person or machine to interpret, predict and, appropriately, respond to the outside. The main characteristic of knowledge representation has two folds: 1) What information is actually made explicit? 2) How the information is physically encoded for subsequent use? A good solution depends on a good representation of knowledge

49. Knowledge Representation (2) There are two kinds of Knowledge: 1) The known world states, or facts, (prior knowledge), 2) Observations (measurements) of the world, obtained by sensors to probe the environment. These observations represent the pool of information, from which examples are used to train the NN

50. Knowledge Representation (3) These Examples can be labeled or unlabeled • In labeled examples • Each example representing an input signal is paired with a corresponding desired response, • Labeled examples may be expensive to collect, as they require availability of a “teacher” to provide a desired response for each labeled example. • Un labeled examples • Unlabeled examples are usually abundant as there is no need for supervision.