Deep learning concepts and neural network basics
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The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.
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Deep learning concepts and neural network basics
- 2. What’s in it for you? Cost function How do neural networks work? Deep learning platforms Introduction to TensorFlow Use case implementation using TensorFlow Applications of deep learning Why is deep learning important? Activation function What are neural networks? What is deep learning?
- 3. Applications of deep learning
- 4. Deep learning helps us make predictions about the rain, earthquakes, tsunamis etc, allowing us to take the required precautions
- 5. With deep learning, machines can comprehend speech and provide the required output
- 6. Deep learning enables the machine to recognize people and objects in the images fed to it
- 7. With deep learning, advertisers can leverage data to perform real-time bidding and targeted display advertising
- 8. What is deep learning?
- 9. Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain
- 10. Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain Artificial Intelligence Machine Learning Deep Learning
- 11. Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain Artificial Intelligence Machine Learning Deep Learning Ability of a machine to imitate intelligent human behavior
- 12. Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain Artificial Intelligence Machine Learning Deep Learning Ability of a machine to imitate intelligent human behavior Application of AI that allows a system to automatically learn and improve from experience
- 13. Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain Artificial Intelligence Machine Learning Deep Learning Ability of a machine to imitate intelligent human behavior Application of AI that allows a system to automatically learn and improve from experience Application of machine learning that uses complex algorithms and deep neural nets to train a model
- 14. Why is deep learning important?
- 15. Works with unstructured data Machine learning works only with large sets of structured and semi-structured data, while deep learning can work with both structured and unstructured data
- 17. Handles complex operations Deep learning algorithms can perform complex operations easily while machine learning algorithms cannot
- 19. Feature Extraction Machine learning algorithms use labeled sample data to extract patterns, while deep learning accepts large volumes of data as input, analyze the input to extract features out of an object
- 21. Achieve best performance Performance of machine learning algorithms decreases as the amount of data increase, so to maintain the performance of the model we need deep learning
- 22. What are neural networks?
- 23. Square With deep learning, a machine can be trained to identify various shapes
- 24. Square With deep learning, a machine can be trained to identify various shapes
- 25. Square With deep learning, a machine can be trained to identify various shapes But, how is the machine able to do this?
- 26. Square With deep learning, a machine can be trained to identify various shapes With neural networks of course!
- 27. A neural network is a system modeled on the human brain inputs outputneuron
- 28. The data is fed as input to the neuron inputs outputneuron
- 29. The neuron processes the information provided as input inputs outputneuron
- 30. The information is transferred over weighted channels inputs outputneuron
- 31. The output is the final value predicted by the artificial neuron inputs outputneuron
- 32. But what exactly happens within a neuron? Note to instructor: Let’s first go through a few concepts that help us understand neural networks
- 35. Within each neuron the following operations are performed: • The product of each input and the weight of the channel it’s passed over is found outputneuron x 1 x2 x3 x4 inputs
- 36. Within each neuron the following operations are performed: • The product of each input and the weight of the channel it’s passed over is found • Sum of the weighted products is computed. This is called the weighted sum inputs outputneuron x 1 x2 x3 x4 x1*w1 + x2*w2 + x3*w3 + x4*w4
- 37. Within each neuron the following operations are performed: • The product of each input and the weight of the channel it’s passed over is found • Sum of the weighted products is computed. This is called the weighted sum • Bias unique to the neuron is added to the weighted sum inputs outputneuron x 1 x2 x3 x4 x1*w1 + x2*w2 + x3*w3 + x4*w4 + bias
- 38. Within each neuron the following operations are performed: • The product of each input and the weight of the channel it’s passed over is found • Sum of the weighted products is computed. This is called the weighted sum • Bias unique to the neuron is added to the weighted sum • The final sum is then subjected to a particular function known as the activation function inputs output x 1 x2 x3 x4 x1*w1 + x2*w2 + x3*w3 + x4*w4 + bias final sum Activation function ( final sum)
- 39. Cost function
- 40. inputs predicted output x 1 x2 x3 x4 neuron y^ The Cost value is the difference between the neural nets predicted output and the actual output from a set of labeled training data y actual output
- 41. inputs predicted output x 1 x2 x3 x4 neuron y^ The Cost value is the difference between the neural nets predicted output and the actual output from a set of labeled training data The least cost value is obtained by making adjustments to the weights and biases iteratively throughout the training process y actual output
- 42. Let’s learn how neural networks work
- 43. How do neural networks work?
- 44. Our neural network will be trained to identify shapes
- 45. 28 28 28*28=784 Our shapes are images of 28*28 pixels
- 46. 28 28 28*28=784 Each pixel is fed as input to the neurons in the first layer
- 47. Each pixel is fed as input to the neurons in the first layer Input layer x1 x2 xn
- 48. Hidden layers improve the accuracy of the output Input layer Hidden layers x1 x2 xn
- 49. Data is passed on from layer to layer over weighted channels Input layer Hidden layers w1 w2 w3 wm x1 x2 xn
- 50. Each neuron in the first hidden layer takes a subset of the inputs and processes it Input layer Hidden layers w1 w2 w3 wm b1 b2 b3 b4 b5 x1 x2 xn
- 51. Each neuron in the first hidden layer takes a subset of the inputs and processes it Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Let’s look into what happens within the neurons
- 52. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Step 1: x1*w1 + x2*w2 + b1 Step 2: Φ(x1* w1 + x2*w2 + b1) where Φ is an activation function
- 53. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Step 1: x1*w1 + x2*w2 + b1 Step 2: Φ(x1* w1 + x2*w2 + b1) where Φ is an activation function The results of the activation function determine which neurons will be activated in the following layer
- 54. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn The results of the activation function determine which neurons will be activated in the following layer w11 w1 2 w1n b1 1 b12 b13 b14 b15
- 55. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn w11 w1 2 w1n b1 1 b12 b13 b14 b15 The results of the activation function determine which neurons will be activated in the following layer
- 56. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Just as before, the information reaching this layer is processed after which a single neuron in the output layer gets activated w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n w2k
- 57. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Just as before, the information reaching this layer is processed after which a single neuron in the output layer gets activated w11 w1 2 w1m b1 1 b12 b13 b14 b15 w2 1 w2m square circle triangle But, our input was a square! What went wrong here?
- 58. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn Just as before, the information reaching this layer is processed after which a single neuron in the output layer gets activated w11 w1 2 w1m b1 1 b12 b13 b14 b15 w2 1 w2m square circle triangle Well, our network needs to be trained first
- 59. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn The predicted output is compared against the actual output by calculating the cost function w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n w2k
- 60. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y The most commonly used cost function is as follows: C=1/2(Y-Y)2^ where Y is the actual value and Y is the predicted value ^ w1n w2k
- 61. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn The cost function determines the error in prediction and reports it back to the neural network w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n w2k
- 62. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 wm x1 x2 xn This is called backpropagation w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n w2k
- 63. Input layer Hidden layers w1 ’ w2’ w3’ b1 b2 b3 b4 b5 wm’ x1 x2 xn The weights are adjusted in order to reduce the error w11 ’ w12 ’ w1n’ b1 1 b12 b13 b14 b15 w21’ w2k’ square circle triangle ^y
- 64. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn The network is trained with the new weights w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle^y w1n’ w2k’ wm’
- 65. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’ ^y C=1/2(Y-Y)2^ Once again, the cost is determined and back propagation is continued until the cost cannot be reduced any further
- 66. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’
- 67. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n’ w2k’ wm’ Similarly, our network must be trained to identify circles and triangles too
- 68. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle ^y w1n’ w2k’ wm’ The weights are thus further adjusted in order to predict the three shapes with the highest accuracy
- 69. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’ We can now rely on our neural network to predict the input shapes 28 28 28*28=784
- 70. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’ We can now rely on our neural network to predict the input shapes 28 28 28*28=784
- 71. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’ We can now rely on our neural network to predict the input shapes 28 28 28*28=784
- 72. Input layer Hidden layers w1 w2 w3 b1 b2 b3 b4 b5 x1 x2 xn w11 w1 2 b1 1 b12 b13 b14 b15 w2 1 square circle triangle w1n’ w2k’ wm’ We can now rely on our neural network to predict the input shapes 28 28 28*28=784
- 74. The primary programming language is LUA with an implementation in C too. Torch’s Python implementation is called PyTorch
- 75. Keras is a Python framework for deep learning. Its USP is reusability of code for CPU and GPU
- 76. TensorFlow is a deep learning platform by Google. It is developed in C++ and has its implementation is Python
- 77. DL4J is the first deep learning library written for Java and Scala. It is integrated with Hadoop and Apache Spark
- 79. • Google’s TensorFlow is currently the most popular deep learning library in the world
- 80. • Google’s TensorFlow is currently the most popular deep learning library in the world • Tensors are vectors or matrices of n dimensions. a m k q d Dimensions 2 4 8 1 1 9 3 2 5 4 4 6 6 3 3 7 8 2 9 5 [5] [5,4] [3,3,3]
- 81. • Google’s TensorFlow is currently the most popular deep learning library in the world • Tensors are vectors or matrices of n dimensions. • In TensorFlow, all computations performed involve tensors
- 82. • Google’s TensorFlow is currently the most popular deep learning library in the world • Tensors are vectors or matrices of n dimensions. • In TensorFlow, all computations performed involve tensors • TensorFlow architecture is as follows Pre-processing data Build a model Train and estimate the model
- 83. Use case implementation with TensorFlow
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