# Artifical Neural Network and its applications

self Employed
5. Nov 2019
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### Artifical Neural Network and its applications

• 2. Agenda Introduction What is ANN? Motivation behind ANN Comparsion between biological neuron & Artficial Neuron Perceptron Feedforward Neural Network Backpropagation Algorithm Types of ANN Pattern Recognition working Future of ANN:Deep learning Deep Learning Application Advantages &Disadvantages Conclusion
• 3. Introduction: Can we code for such different patterns ???? Human Vision
• 4. What is Artifical Neural Network ? Neural Network Neurons Connections between them An artificial neuron network (ANN) is a computational i.e.,an information processing model that is inspired by the way biological nervous systems(such as the brain) process the information. ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found.
• 6. Comparsion between Biological Neuron & artifical Neuron Biological Neural Network(BNN) Artifical Neural Network(ANN) Soma Node Dendrites Input Synapse Weights or interconections Axon Output
• 7. Perceptron A perceptron is a most fundamental unit of neural network(Artificial Neurons) that does certain computations to detect feature or business intelligence in the input data. The perceptron is a linear model for supervised learning used for Binary Classification. Perceptron consist of 4 parts: i. input ii. weights & bias iii. summation function iv. Activation Function v. Output Perceptron Learning Rule Perceptron learns the weights for the input signals in order to draw a linear decision boundary Two types of perceptron: a) Single Layer Perceptron b) Multi-layer perceptron
• 8. Activation Functions Sigmoid Fuction Step function ReLu Function Tanh function
• 10. Feed-Forward Neural Network: It is also known as Multi-layered Neural Network Information only travels forward in the neural network, through the input nodes then through the hidden layers(1 or more)and finally through the output nodes. Capable of handling the non-linearly separable data Layer present between input and output layer are called HIDDEN Layers.
• 11. Backpropagation Algorithm: Backpropagation, an abbreviation for ”Backward propagation of errors” is common method of training ANN. The method calculates the gradient of a loss function w.r.t all the weights in the network. The gradient is fed to the optimizer in order to minimize the loss function. The Backpropagation algorithm looks for the minimum value of the error function in weight space using a technique called the delta rule or gradient descent. The backpropagation learning algorithm can be divided into two phases i. Forward Propagation(propagate) ii. Backward Propagation(update weights)
• 12. Algorithm Step 1:Initializtion Randomly set all the weights threshold levels of n/w. Step 2: Forward computing: compute the hidden vector h on hidden layer zj =φ(∑i vijxi) compute the o/p vector y on o/p layer yk= φ(∑i wjkzj) Step 3: Calculate the Total Error Check the difference between y(actual o/p) and ŷ (predicted o/p) E=1/2(y-ŷ)2 Step 4: Backward computing Finding the derivative of the error Calculating the partial derivative of the error w.r.t weight Update the weights: ∆ wj = - ɳ ∂E ∂wij wj=wj+ ∆ wj
• 13. Example: Consider the below table Input Desired output 0 0 1 2 2 4 Input Desired O/p Model O/p(w=3) 0 0 0 1 2 3 2 4 6 Input Desire d o/p Model o/p (w=3) Square Error (y-ŷ)2 Model o/p (W=4) Square Error (w=4) 0 0 0 0 0 0 1 2 3 1 4 4 2 4 6 4 8 16 Input Desired o/p Model o/p (w=3) Square Error (y-ŷ)2 Model o/p (W=2) Square Error (w=2) 0 0 0 0 0 0 1 2 3 1 2 0 2 4 6 4 4 0
• 14. Use Case : Classify leaf images as either Diseased or Non-Diseased
• 15. 1.Compute the weights and according to that check the probability of desired output
• 16. If the predicted output is wrong then by using Backpropagation learning again train the neural net Update the weights by propagating backward
• 17. After updating the weights again forward compute the ouptut i.e.,hence we classified the Diseased and Non-diseased leaf
• 19. Use CASE: Pattern Recognition Data Acquistion &pre-processing Segmentation Feature Extraction Classification & Recognition
• 24. Future of ANN: Deep Learning Artificial Intelligence ,Machine Learning and Deep Learning are interconnected fields Machine learning and Deep learning aids AI by providing a set of algorithms and Neural Net to solve a data driven problems
• 25. Deep Learning Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled
• 30. Advantages & Disadvantages of ANN Parallel processing ability Information is stored on an entire network ,not just a database Fault tolerance means corruption of one or more cells of the ANN will not stop the generation of output Gradual Corruption means the network will slowly degrade over time, instead of a problem destroying the network instantly The lack of rules for determining the proper network structure The requirement of processors with parallel processing abilities makes ANN hardware dependent The lack of explanation behind probing solutions Generation of lack of trust in the network Advantages Disadvantages