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1. NCCT Centre for Advanced Technology ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ SOFTWARE DEVELOPMENT * EMBEDDED SYSTEMS #109, 2nd Floor, Bombay Flats, Nungambakkam High Road, Nungambakkam, Chennai - 600 034. Phone - 044 - 2823 5816, 98412 32310 E-Mail: ncct@eth.net, esskayn@eth.net, URL: ncctchennai.com Dedicated to Commitments, Committed to Technologies

2. NEURAL NETWORKS & ITS APPLICATIONS NCCT Where Technology and Solutions Meet

15. BIOLOGICAL NEURAL NETWORK NCCT

16. The Neuron as a Simple Computing Element DIAGRAM OF A NEURON

17. Analogy between Biological and Artificial Neural Networks

18. ARCHITECTURE OF A TYPICAL ARTIFICIAL NEURAL NETWORK

28. SINGLE-LAYER TWO-INPUT PERCEPTRON

31. THE PERCEPTRON LEARNING RULE where p = 1, 2, 3, . . .  is the learning rate, a positive constant less than unity. The perceptron learning rule was first proposed by Rosenblatt in 1960. Using this rule we can derive the perceptron training algorithm for classification tasks.

32. STEP 1: INITIALISATION Set initial weights w 1, w 2,…, wn and threshold  to random numbers in the range [  0.5, 0.5]. PERCEPTRON’S TRAINING ALGORITHM STEP 2: ACTIVATION Activate the perceptron by applying inputs x1(p), x2(p),…, xn(p) and desired output Yd (p). Calculate the actual output at iteration p = 1 where n is the number of the perceptron inputs, and step is a step activation function.

33. STEP 3: WEIGHT TRAINING Update the weights of the perceptron where is the weight correction at iteration p. The weight correction is computed by the delta rule: where STEP 4: ITERATION Increase iteration p by one, go back to Step 2 and repeat the process until convergence. PERCEPTRON’S TRAINING ALGORITHM

35. ACTIVATION FUNCTIONS

36. EXAMPLE A neuron uses a step function as its activation function q = 0.2 and W1 = 0.1, W2 = 0.4, What is the output with the following values of x1 and x2: 1 1 0 1 1 0 0 0 Y x2 x1

39. NETWORK ARCHITECTURES SINGLE LAYER FEED FORWARD Input layer of source nodes Output layer of neurons NCCT

40. NETWORK ARCHITECTURES MULTI LAYER FEED-FORWARD INPUT LAYER OUTPUT LAYER HIDDEN LAYER 3-4-2 NETWORK NCCT

42. NEURAL NETWORK ARCHITECTURES

44. FACE RECOGNITION 90% accurate learning head pose, and recognizing 1-of-20 faces

45. HANDWRITTEN DIGIT RECOGNITION

46. Projects @ NCCT Redefining the Learning Specialization, Design, Development and Implementation with Projects Experience the learning with the latest new tools and technologies…

54. Placements @ NCCT NCCT has an enormous placement wing, which enrolls all candidates in its placement bank, and will keep in constant touch with various IT related industries in India / Abroad, who are in need of computer trained quality manpower Each candidate goes through complete pre-placement session before placement made by NCCT The placement division also helps students in getting projects and organize guest lectures, group discussions, soft learning skills, mock interviews, personality development skills, easy learning skills, technical discussions, student meetings, etc., For every student we communicate the IT organizations, with the following documents * Curriculum highlighting the skills * A brief write up of the software knowledge acquired at NCCT, syllabus taught at NCCT * Projects and Specialization work done at NCCT * Additional skills learnt

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