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# Introduction to artificial neural networks

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Basics of ANN, introduction to object recognition

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### Introduction to artificial neural networks

1. 1. Introduction to Artificial Neural Networks Object Recognition Problem Piyush Mishra (Mtech Production, 217ME2221) Department of Mechanical Engineering, NIT Rourkela
2. 2. x1 x2 x3 w1 w2 w3 y1 y2 y3 Outputs to other neurons Inputs from neurons ANALOGY
3. 3. • A child doesn't know whether the object in front of him is a CAT or a DOG…we need to tell him that (a) is Dog and (b) is Cat. In other words we TRAIN him/he LEARNS about various objects.(a) (b)
4. 4. Neural Networks are also trained By showing them many images of cats and dogs. But will they give the correct answer in first attempt?
5. 5. No!! Input (Dendrites) Output (Synapse) Hidden Layer/Processing layer (Cell body +Axon) More the line width, more the weight To get correct answer, the weights are adjusted. This is called training the ANN, more specifically supervised learning. DOG
6. 6. Correct output is obtained after the learning process Picture ref.[1]
7. 7. Convolutional neural networks Picture ref.[2]
8. 8. Summary- The Big Picture • Imagine the box below to be our brain. Our brain contains billions of neuron. • Each neurons contains weights and thresholds (W & T). W T T W W W W W T T T T T W W T T T W W W Ref.[3] X1 Xm Z1 Zm Z=f(x,w,t) • Output Z is a function of inputs ‘x’, weights ‘w’ and thresholds ‘t’. • We have to adjust the weights and thresholds so that what we get out is what we want.
9. 9. References [1] The art of neural networks, Mike Tyka, TEDXTUM, https://youtu.be/0qVOUD76JOg [2] Introduction to Deep Learning: What Are Convolutional Neural Networks? MATLAB, https://youtu.be/ixF5WNpTzCA [3] 6.034, Fall 2015, Artificial Intelligence, Patrick H. Winston, Lec 12a, MIT OCW, https://youtu.be/uXt8qF2Zzfo