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CNNs.pdf

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Ben Wycliff Mugalu

The document provides an overview of convolutional neural networks (CNNs) presented by Ben Wycliff Mugalu. CNNs take images as input, identify key features through convolutional and pooling layers to classify objects. The document explains how CNNs mimic human vision through feature extraction and shows examples of convolution and max pooling operations on sample images to extract prominent features.

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Convolutional Neural Networks Presented By: Ben Wycliff Mugalu (Machine Learning Engineer) An Introductory Lecture
Ben Wycliff Mugalu ben12wycliff@gmail.com What is a CNN? A convolutional neural network is a deep learning algorithm that takes in an image, identifies the key features of the object in that image and assign importance to distinguish it from other objects. Input Image Output label CNN (Convolutional Neural Network)
Ben Wycliff Mugalu ben12wycliff@gmail.com CNNs mimic the human visual perception and cognition process Humans classify (learn) objects by identifying their features.
Ben Wycliff Mugalu ben12wycliff@gmail.com CNNs mimic the human visual perception and cognition process
Ben Wycliff Mugalu ben12wycliff@gmail.com Simple Facial Expression Classification Sad CNN (Convolutional Neural Network) Happy CNN (Convolutional Neural Network)
Ben Wycliff Mugalu ben12wycliff@gmail.com How CNNs Learn Max Pooling STEP 2 Convolution and ReLu activation STEP 1 Flattening STEP 3 Full Connection STEP 4
Ben Wycliff Mugalu ben12wycliff@gmail.com Images Gray scale (Black and White) RGB (Colored Images) 3 channels 2D array Pixel 0 ≤ pixel value ≤ 255 3D array
Ben Wycliff Mugalu ben12wycliff@gmail.com 7 X 7 sad face image representation 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
Ben Wycliff Mugalu ben12wycliff@gmail.com Convolution Convolution is a mathematical way of combining two signals to form a third signal. g = f⊗h
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 ⊗ = Image Kernel Feature Map
Ben Wycliff Mugalu ben12wycliff@gmail.com ReLU layer Convolutional Layer F e a t u r e m a p s Rectifier activation function We use the rectifier activation function to eliminate linearity since images in their nature are not linear.
Ben Wycliff Mugalu ben12wycliff@gmail.com Max Pooling Max pooling reduces the size of the image by extracting the most prominent features. Reduces the computation power requirement (down samples pixels) Improves the neural network ability to generalize on unseen data since it filters out the most present features.
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 2 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
Ben Wycliff Mugalu ben12wycliff@gmail.com Flattening Flattening is done to obtain a 1 dimensional vector to serve as an input to the artificial neural network (fully connected layer). Pooled Feature m aps ANN input layer Max Pooling Layer
Ben Wycliff Mugalu ben12wycliff@gmail.com Flattening 1 1 1 3 2 1 1 2 2 1 1 1 3 2 1 1 2 2 Flattening
Ben Wycliff Mugalu ben12wycliff@gmail.com Fully Connected Layer ★ All inputs from one layer are connected to every activation unit ★ Input layer receives flattened vector ★ Hidden layer performs non linear transformations of the network inputs ★ The output layer translates the weights into classes.
Ben Wycliff Mugalu ben12wycliff@gmail.com Backwards Propagation ★ Several iterations (Epochs) are made while training the neural network, an error rate is obtained for every iteration. ★ Backwards propagation is a method of fine-tuning the weights in the neural network basing on the error rate obtained from the previous epoch.
Ben Wycliff Mugalu ben12wycliff@gmail.com Alternative to fully connected layer ★ Some methods use traditional classification algorithms such as SVMs and Random Forest after feature extraction. ★ Tradition methods can yield better accuracy if the dataset is very small. ★ With a sufficient dataset size, an end to end neural network will certainly provide better performance and accuracy. ★ Transfer learning can also be employed to train a CNN with limited data.
Ben Wycliff Mugalu ben12wycliff@gmail.com Application of CNNs ★ Face recognition ★ Climate change monitoring ★ Disease diagnosis ★ Analysing documents ★ Object recognition and detection ★ Advertisements
Ben Wycliff Mugalu Machine Learning Engineer ben12wycliff@gmail.com The End

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CNNs.pdf

  • 1. Convolutional Neural Networks Presented By: Ben Wycliff Mugalu (Machine Learning Engineer) An Introductory Lecture
  • 2. Ben Wycliff Mugalu ben12wycliff@gmail.com What is a CNN? A convolutional neural network is a deep learning algorithm that takes in an image, identifies the key features of the object in that image and assign importance to distinguish it from other objects. Input Image Output label CNN (Convolutional Neural Network)
  • 3. Ben Wycliff Mugalu ben12wycliff@gmail.com CNNs mimic the human visual perception and cognition process Humans classify (learn) objects by identifying their features.
  • 4. Ben Wycliff Mugalu ben12wycliff@gmail.com CNNs mimic the human visual perception and cognition process
  • 5. Ben Wycliff Mugalu ben12wycliff@gmail.com Simple Facial Expression Classification Sad CNN (Convolutional Neural Network) Happy CNN (Convolutional Neural Network)
  • 6. Ben Wycliff Mugalu ben12wycliff@gmail.com How CNNs Learn Max Pooling STEP 2 Convolution and ReLu activation STEP 1 Flattening STEP 3 Full Connection STEP 4
  • 7. Ben Wycliff Mugalu ben12wycliff@gmail.com Images Gray scale (Black and White) RGB (Colored Images) 3 channels 2D array Pixel 0 ≤ pixel value ≤ 255 3D array
  • 8. Ben Wycliff Mugalu ben12wycliff@gmail.com 7 X 7 sad face image representation 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
  • 9. Ben Wycliff Mugalu ben12wycliff@gmail.com Convolution Convolution is a mathematical way of combining two signals to form a third signal. g = f⊗h
  • 10. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 ⊗ = Image Kernel Feature Map
  • 11. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 ⊗ = Image Kernel Feature Map
  • 12. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 ⊗ = Image Kernel Feature Map
  • 13. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 ⊗ = Image Kernel Feature Map
  • 14. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 ⊗ = Image Kernel Feature Map
  • 15. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 ⊗ = Image Kernel Feature Map
  • 16. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 ⊗ = Image Kernel Feature Map
  • 17. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 ⊗ = Image Kernel Feature Map
  • 18. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 ⊗ = Image Kernel Feature Map
  • 19. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 ⊗ = Image Kernel Feature Map
  • 20. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 ⊗ = Image Kernel Feature Map
  • 21. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 ⊗ = Image Kernel Feature Map
  • 22. Ben Wycliff Mugalu ben12wycliff@gmail.com 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 Convolution 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 ⊗ = Image Kernel Feature Map
  • 23. Ben Wycliff Mugalu ben12wycliff@gmail.com ReLU layer Convolutional Layer F e a t u r e m a p s Rectifier activation function We use the rectifier activation function to eliminate linearity since images in their nature are not linear.
  • 24. Ben Wycliff Mugalu ben12wycliff@gmail.com Max Pooling Max pooling reduces the size of the image by extracting the most prominent features. Reduces the computation power requirement (down samples pixels) Improves the neural network ability to generalize on unseen data since it filters out the most present features.
  • 25. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 26. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 27. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 28. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 29. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 30. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 31. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 32. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 33. Ben Wycliff Mugalu ben12wycliff@gmail.com How Max Pooling Works 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 3 2 1 0 1 0 1 2 2 Feature Map 1 1 1 3 2 1 1 2 2 Pooled features Max Pooling Stride = 1 Kernel = 2✕2
  • 34. Ben Wycliff Mugalu ben12wycliff@gmail.com Flattening Flattening is done to obtain a 1 dimensional vector to serve as an input to the artificial neural network (fully connected layer). Pooled Feature m aps ANN input layer Max Pooling Layer
  • 35. Ben Wycliff Mugalu ben12wycliff@gmail.com Flattening 1 1 1 3 2 1 1 2 2 1 1 1 3 2 1 1 2 2 Flattening
  • 36. Ben Wycliff Mugalu ben12wycliff@gmail.com Fully Connected Layer ★ All inputs from one layer are connected to every activation unit ★ Input layer receives flattened vector ★ Hidden layer performs non linear transformations of the network inputs ★ The output layer translates the weights into classes.
  • 37. Ben Wycliff Mugalu ben12wycliff@gmail.com Backwards Propagation ★ Several iterations (Epochs) are made while training the neural network, an error rate is obtained for every iteration. ★ Backwards propagation is a method of fine-tuning the weights in the neural network basing on the error rate obtained from the previous epoch.
  • 38. Ben Wycliff Mugalu ben12wycliff@gmail.com Alternative to fully connected layer ★ Some methods use traditional classification algorithms such as SVMs and Random Forest after feature extraction. ★ Tradition methods can yield better accuracy if the dataset is very small. ★ With a sufficient dataset size, an end to end neural network will certainly provide better performance and accuracy. ★ Transfer learning can also be employed to train a CNN with limited data.
  • 39. Ben Wycliff Mugalu ben12wycliff@gmail.com Application of CNNs ★ Face recognition ★ Climate change monitoring ★ Disease diagnosis ★ Analysing documents ★ Object recognition and detection ★ Advertisements
  • 40. Ben Wycliff Mugalu Machine Learning Engineer ben12wycliff@gmail.com The End