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Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

This Edureka "Neural Network Tutorial" tutorial will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.

Below are the topics covered in this tutorial:

1. Why Neural Networks?
2. Motivation Behind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks

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Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

  1. 1. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Problem Statement Using Artificial Neural Network, we need to figure out, if the bank notes are real or fake?
  2. 2. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Problem Statement Using Artificial Neural Network, we need to figure out, if the bank notes are real or fake?
  3. 3. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Data Set Data were extracted from images that were taken from genuine and forged banknote-like specimens. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images. • Variance of Wavelet Transformed image • Skewness of Wavelet Transformed image • Curtosis of Wavelet Transformed image • Entropy of image Features 1 – Real, 0 - Fake Label
  4. 4. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Implementation Steps Start Read the Dataset Define features and labels Divide the dataset into two parts for training and testing TensorFlow data structure for holding features, labels etc.. Implement the model Train the model Reduce MSE (actual output – desired output) End Repeat the process to decrease the loss Pre-processing of dataset Make prediction on the test data
  5. 5. Agenda ▪ Why Neural Networks? ▪ Motivation Behind Neural Networks ▪ What Are Neural Networks? ▪ Single Layer Perceptron ▪ Multi Layer Perceptron ▪ Implementation Of The Use-Case ▪ Applications Of Neural Networks
  6. 6. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Why Neural Network? We will begin by looking at the reason behind the introduction of Neural Networks
  7. 7. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Problem Before Neural Networks • Unless the specific steps that the computer needs to follow are known the computer cannot solve a problem. • This restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. The computer follows a set of instructions in order to solve a problem
  8. 8. Copyright © 2017, edureka and/or its affiliates. All rights reserved. After Neural Networks With Neural Networks computers can do things that we don't exactly know how to do. Neural Networks learn by example. They cannot be programmed to perform a specific task
  9. 9. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Motivation Behind Neural Networks Let’s see the motivation behind Artificial Neural Network.
  10. 10. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Motivation Behind Neural Networks Neuron Dendrite: Receives signals from other neurons Cell Body: Sums all the inputs Axon: It is used to transmit signals to the other cells The building block of a neural net is the neuron. An artificial neuron works much the same way the biological one does.
  11. 11. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What Are Artificial Neural Networks? Now is the correct time to understand Artificial Neural Networks
  12. 12. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What Are Artificial Neural Networks? Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. Training Where is dog in this pic? Input Without Neural Network
  13. 13. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What Are Artificial Neural Networks? Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. Training There is one dog in this pic Input With Neural Network
  14. 14. Copyright © 2017, edureka and/or its affiliates. All rights reserved. How It Works? Let’s focus on how Neural networks work?
  15. 15. Copyright © 2017, edureka and/or its affiliates. All rights reserved. How Artificial Neural Networks Work? To get started, I'll explain artificial neuron called a perceptron. X1 X2 Xn Processing Element S = Xi Wi Y W1 W2 Wn Y1 Y2 Yn F(S) Summation Transfer Function Outputs Artificial Neuron Biological Neuron
  16. 16. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Modes In Perceptron Using ModeTraining Mode In the training mode, the neuron can be trained to fire (or not), for particular input patterns In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output
  17. 17. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Activation Functions Let’s look at various types of Activation Functions
  18. 18. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Activation Function Step Function Sigmoid Function Sign Function
  19. 19. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Perceptron Training With Analogy Let’s understand this with an analogy
  20. 20. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Perceptron Learning Algorithm – Beer Analogy Suppose you want to go to a beer festival happening near your house. So your decision will depend on multiple factors: 1. How is the weather? 2. Your wife is going with you? 3. Any public transport is available?
  21. 21. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Inputs
  22. 22. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Output Output ‘O’ 1 0
  23. 23. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Let’s Prioritize Our Factors X1 = 1 Output =1 Suppose for you the most important factor is weather, if it is not good you will definitely don’t go. Even if the other two inputs are high. If it is good than definitely you will go.
  24. 24. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Assign Weights Now, let’s assign weights to our three inputs
  25. 25. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Assign Weights W1 = 6, W2 = 2, W3 = 2 Threshold = 5 W1 = 6, W2 = 2, W3 = 2 Threshold = 3 It will fire when weather is good and won’t fire if weather is bad irrespective of the other inputs It will fire when either x1 is high or the other two inputs are high W1 = Weight associated with input X1 W2 = Weight associated with input X2 W3 = Weight associated with input X3
  26. 26. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Multilayer Perceptron – Artificial Neural Network Now, let’s look at multilayer perceptron
  27. 27. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Multilayer Perceptron – Artificial Neural Network As you know our brain is made up of millions of neurons, so a Neural Network is really just a composition of perceptrons, connected in different ways and operating on different activation functions. Input Layer Hidden Layer 1 Hidden Layer 2 Output Layer
  28. 28. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Example Of Artificial Neural Networks Let’s see an example where an Artificial Neural Network is used for image recognition
  29. 29. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Artificial Neural Network - Example
  30. 30. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Let’s see how to train a Neural Network or a Multilayer Perceptron
  31. 31. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network The most common deep learning algorithm for supervised training of the multi-layer perceptrons is known as backpropagation. In it, after the weighted sum of inputs and passing through the activation function we propagate backwards and update the weights to reduce the error (desired output – model output). Consider the below example: Input Desired Output 0 0 1 1 2 4
  32. 32. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Input Desired Output Model Output (W=3) 0 0 0 1 2 3 2 4 6 Let’s consider the initial value of the weight as 3 and see the model output
  33. 33. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Input Desired Output Model Output (W=3) Absolute Error Square Error 0 0 0 0 0 1 2 3 1 1 2 4 6 2 4 Now, we will see the error (Absolute and Square)
  34. 34. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Input Desired Output Model Output (W=3) Absolute Error Square Error Model Output (W=4) 0 0 0 0 0 0 1 2 3 1 1 4 2 4 6 2 4 8 Let’s update the weight value and make it as 4
  35. 35. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Input Desired Output Model Output (W=3) Absolute Error Square Error Model Output (W=4) Square Error 0 0 0 0 0 0 0 1 2 3 1 1 4 4 2 4 6 2 4 8 16 Still there is error, but we can see that error has increased
  36. 36. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network W Error W Error
  37. 37. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Training A Neural Network Square Error Weight Decrease Weight Increase Weight
  38. 38. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Implementation Steps Start Read the Dataset Define features and labels Divide the dataset into two parts for training and testing TensorFlow data structure for holding features, labels etc.. Implement the model Train the model Reduce MSE (actual output – desired output) End Repeat the process to decrease the loss Pre-processing of dataset Make prediction on the test data
  39. 39. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Applications Of Neural Networks Modelling and Diagnosing the Cardiovascular System Electronic noses Neural Network In Medicine Neural Network In Business Marketing Credit Evaluation
  40. 40. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Session In A Minute Why Neural Networks? What is Neural Network? What is Perceptron? What is Multi Layer Perceptron? Training Of Multi Layer Perceptron Use-Case Implementation
  41. 41. Copyright © 2017, edureka and/or its affiliates. All rights reserved.

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