Part II: Activation and Pooling Layers The activation layers and pooling layers of the Convolutional Neural Networks (CNN) will be introduced, following the first part of this series where the convolutional layers were explained. Activation Layers Normally, the feature maps are passed through an activation layer with the purpose of Read more…
Gradient Descent is an optimization algorithm for finding the local minimum of a function. It searches for the combination of parameters that minimizes the cost function. For the Gradient Descent to work, the cost function must be differentiable and convex. Visualization of Gradient Descent. Cost Function vs. Weight (W). Source: Read more…
Part I: Convolutional Layers Convolutional Neural Networks (CNN) are a kind of deep learning algorithms that can take multidimensional data such as images as input and detect and extract patterns or characteristics within it. CNNs have been proven to be very useful in Computer Vision.These networks apply different kinds of Read more…