Unravelling the mysteries of Artificial Intelligence

“Any sufficiently advanced technology is indistinguishable from magic.” - Arthur C. Clarke. Well! Convolutional Neural Network (CNN) is such a technology. How it does, what it does is truly indistinguishable from magic. Read our earlier post - “From Cats to Convolutional Neural Networks” , to understand why CNNs come close to human intelligence. Although the inner workings of a CNN can be explained, the magic remains. Fascinated by CNNs, we thought of coming up with as many questions about CNNs to understand the mystery of why it is able to classify images or any kind of input so well. What is convolution? What is pooling? Which pooling function is preferred - Max or Average? What is the role of activation functions in CNN? Why is Relu prefered in CNN rather than Sigmoid? Why adding more layers increase the accuracy of the network? What is the intuition behind CNN? What is stride? Is it necessary to include zero-padding? What is parameter ...

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields. The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors. The first neural network model that drew inspiration from the hierarchy model of the visual nervous system proposed by Hubel and Wiesel was Neocognitron invented by Kunihiko Fukushima, and had the ability of performing unsupervised learning. Kunihiko Fukushima’s approach was commendable as it was the first neural network model having the capability of pattern recognition similar to human brain. The model gave a lot of insight and helped future understanding of the brain. A successful ad...