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![Float Array
A Typical Neural Network
Input
Layer
Hidden Layers
(black box)
Output
Layer
[input pattern] [output pattern]
many different architectures define the
interaction between the input and the
output layer
Float Array
[ -0.025, 0.23, 0.44 ] [ 0.712, 0.471 ]](https://image.slidesharecdn.com/howmachinelearningischangingtheworld-170217040618/75/How-machine-learning-is-changing-the-world-9-2048.jpg)
Uploaded byEmilio Garcia
How machine learning is changing the world
The document discusses how machine learning, and specifically deep learning using neural networks, is changing the world. It provides an overview of key concepts like artificial intelligence, machine learning, and deep learning. It then discusses the history and basic concepts of neural networks like neurons, layers, activation functions, and optimization. Finally, it discusses applications of deep learning like computer vision, natural language processing, and more.
How machine learning is changing the world
- 1. How Machine Learningis Changing the World DEEP LEARNING WITH TENSORFLOW Emilio Garcia @unindanachado
- 3. Agenda ● Motivation ● Keyconcepts ○ AI, ML & DL ● Brief revision on ANN ○ Neurons and Layers ○ Activation and Loss Functions ○ Optimization ● Deep Learning ○ Convolutions ○ Architectures ● TensorFlow Basis ● Demo Time
- 6. Key Concepts Artificial Intelligence Machine Learning Deep Learning “any techniquethat enables computers to mimic human intelligence" “subset of AI that includes abstruse statistical techniques that enable machines to improve at tasks with experience" “algorithms that permit software to train itself to perform tasks”
- 7. Deep Learning =Convolutional Neural Networks
- 8. Little History ofNeural Networks 1943: McCulloch, W. and Pitts, W. first introduced the idea of a neural network. 1958: Rosenblatt, F introduced backpropagation. 2006: Hinton, G provided a radical new way to train deep neural networks. Today: Graphic Process Units allow programmers to train networks with several layers.
- 9. Float Array A TypicalNeural Network Input Layer Hidden Layers (black box) Output Layer [input pattern] [output pattern] many different architectures define the interaction between the input and the output layer Float Array [ -0.025, 0.23, 0.44 ] [ 0.712, 0.471 ]
- 10. Considerations ● Input datahas to be normalized ● Most types of ANNs don’t care about order in train data ● Some others do care: BAM (Bidirectional Associative Memory) ● Certain Types perform better in certain Problem Domains
- 11. Network Types &Problem Domains Clust Regis Classif Predict Robot Vision Optim Self-Organizing Map ●●● ● ● Feedforward ●●● ●●● ●● ●● ●● Boltzmann Machine ● ●● Deep Belief Network ●●● ●● ●● Deep Feedforward ●●● ●●● ●● ●●● ●● Recurrent Network ●● ●● ●●● ●● ● Convolutional Network ● ●●● ●●● ●●● Deep Learning and Neural Networks (Jeff Heaton)
- 12. Node, Neuron, Unit Input 1 Input 2 Input 3 Neuron ActivationFunction Output Neuron output: x: inputs w: weights : activation function weight 1 weight 2 weight 3
- 13. Neuron Types I1 I2B1 B2 B3 N1 N1 N2 O1 N2 Input 1 Hidden 1 Hidden 2 Context 1 Context 2 Output 1 w2w1 copycopy w5 w3 w4 w6 Input Output BiasHidden Context
- 14. Activation Functions Linear Threshold Alsocalled transfer functions. They establish bounds for output of the neurons. Some of the most popular include: First used in the original perceptron (McCulloch & Pitts, 1943) Commonly found in output layers of regression networks
- 15. Activation Functions Sigmoid HyperbolicTangent ReLU Used to ensure that values are compressed between 0 and 1. Values range from -1 to 1, mean remains 0. Antisymmetric AFs yield faster convergence. Linear, non-saturating function.
- 16. ● Usually foundin the output layer ● Represents the probability that the input falls into each class The Softmax Activation Function i: index of the output neuron j: indexes of all neurons in the group z: array of output neurons
- 17. Bias ● The weightsof the neuron allow us to adjust the slope or shape of the activation function. ● Whereas Bias shift left/right the sigmoid curve. f(x, 0.5, 0) f(x, 1.0, 0) f(x, 1.5, 0) f(x, 1.0, 1.0) f(x, 1.0, 0.5) f(x, 1.0, 1.5)
- 18. What about convolutions? “Inimage processing, a kernel, convolution matrix, or mask is a small matrix. It is useful for blurring, sharpening, embossing, edge detection, and more. This is accomplished by means of convolution between a kernel and an image.” -wikipedia-
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- 23. Deep Convolutional NeuralNetwork
- 24. Deep Convolutional NeuralNetwork 13 Layer CNN - Alex Krizhevsky (2012) 22 Layer CNN - GoogLeNet: Inception v3 (2014)
- 25. Learning to RefineObject Segments - Pedro O. Pinheiro DeepMask and SharpMask
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- 27. Other Real-World Applications ●Self-Driving Cars ● Medical Image Analysis ● Bioinformatics ● Industry: ○ Churn Prediction ○ Sentimental Analysis ○ Chatboots ○ Recommendation Systems ○ Financial Evaluation ● Politics ● Security
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- 29. Recommended Material &Contact Info Pattern Classification Richard O. Duda ISBN-13: 978-0471056690 ISBN-10: 0471056693 Personal (Work and Academic): emilio.garcia@santexgroup.com emilio.garcia@pucp.edu.pe GRPIAA: http://inform.pucp.edu.pe/~grpiaa/ https://www.facebook.com/grpiaa
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