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Sangeeta TiwariFolgen

5. Nov 2019•0 gefällt mir•1,405 views

5. Nov 2019•0 gefällt mir•1,405 views

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Technologie

It is a presentation that acquaints you with the latest technology that can recognise patterns i.e neural networks and some of its applications.

Sangeeta TiwariFolgen

- 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.
- 5. Motivation behind Artificial Neural Network
- 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
- 9. Single-layer Perceptron Example: Limitation of Single-Layer Perceptron:
- 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
- 18. Types of ANN
- 19. Use CASE: Pattern Recognition Data Acquistion &pre-processing Segmentation Feature Extraction Classification & Recognition
- 20. Example: 1 0
- 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
- 27. Voice Assistant
- 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
- 31. Any Queries ???