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CNN Machine learning DeepLearning
- 1. Convolutional Neural Network • Abhishek Sharma(1551244) • Sagar Sangam (1551207) • Deepak Prasad (1551060)
- 2. What is CNN? WhyCNN?
- 3. What do we have? •Computer vision • CNN into picture and it’s history • Architecture and Working
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- 6. • The visionbegins with eyes, but truly take places in the Brain • It took mother nature over 500 million years to create a system to bring the ability of recognizing things. • The collaboration between the eyes and the brain, called the primary visual pathway, is the reason we can make sense of the world around us.
- 7. How does thecomputer see the world?
- 8. Human Vision ComputerVision
- 9. • Computer ‘see’in a different way than we do. • Every image can be represented as a 2-dimensional arrays of numbers, called pixels. Pixels
- 10. • What todo with these pixels? • Ultimately we want machines to see like we do naming objects identifying peoples, inferring 3d understanding emotions, actions and so on. • The first step towards this goal is to teach the computer to identify objects and for this the only pixels are not enough.
- 11. Teach a computerto identify a Cat
- 16. • Even somethinga simple as a household pet can present an infinite number of variations to the object model, and that ‘s just one thing. • Similarly to how a child learns to recognise objects, we need to show an algorithm millions of picture before it is able to generalize the input and make predictions for images it has never seen before. • Here comes Convolution Neural Network into picture.
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- 18. • To teachan algorithm how to recognize objects in images, we use a specific type of Artificial Neural Network: a Convolutional Neural Network (CNN). • It is a special type of Neural Network widely used for recognize the image.
- 19. Where it allbegins ? • Convolutional Neural Networks are inspired by the brain. Research in the 1950s and 1960s by D.H Hubel and T.N Wiesel on the brain of mammals suggested a new model for how mammals perceive the world visually. • In 1980, a researcher called Fukushima proposed a hierarchical neural network model. He called it the Neocognitron. • The Neocognitron was able to recognise patterns by learning about the shapes of objects
- 20. The ‘pixels’of the object used as the input to CNN to recognize the object; earlier the only pixels were not enough to identify a certain object.
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- 22. Regular Neural Networkand CNN
- 23. Architecture Convolutional Neural Networksare a bit different from Regular Neural Network. First of all, the layers are organized in 3 dimensions: width, height and depth. Further, the neurons in one layer do not connect to all the neurons in the next layer but only to a small region of it. Lastly, the final output will be reduced to a single vector of probability scores, organized along the depth dimension.
- 24. CNNs have twocomponents: • The Hidden layers/Feature extraction part In this part, the network will perform a series of convolutions with RELU and pooling operations during which the features are detected. • The Classification part The fully connected layers will serve as a classifier on top of these extracted features. They will assign a probability for the object on the image being what the algorithm predicts it is.