Image Classification with Localization

In image classification, we feed input image to a convolutional neural network and it gives back a feature vector (fully connected layer). The feature vector is then injected to softmax layer to get prediction of a class. Let's say we are building an image classifier for self-driving car application with ... read more

Why Convolution is Useful in Neural Networks?

You may wonder why convolution layers are so useful when we include them into neural networks. There are essentially two advantages convolution layers have over fully connected layers; 1. Parameter Sharing: Let's say we have 32x32x3 input image and convolve it with six 5x5 filters. This would results into an ... read more

Convolutional Neural Networks (CNN)

Computer vision has seen great success during last few years, mainly due to advances in deep learning. Applications like unlock of phone and house doors using face recognition, and classification of pictures in smartphone's gallery app are all direct consequences of application of deep learning in computer vision. To be ... read more

Machine Learning Strategy

In modern big data era, machine learning strategy plays significant role in ultimate fate of a machine learning project. Let's say we have trained a classifier with 90% accuracy on test examples. But that accuracy is not be good enough for the application. To improve classifier’s accuracy, we can try a ... read more

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