Data Science,Deep Learning,Neural Network

Deep Learning – is yet another buzz word and is the successor of Machine Learning and now a predecessor to Artificial Intelligence or simply put – AI. The intriguing thing is the number of articles that exist in our enterprise ecosystem, all emphatically pressing on the implementation of deep learning to solve real world problems. However most of them restricts themselves to uncomfortable algebra and calculus which too seems to be “inspired” from Stanford Courses (In case you are not aware of those, try googling CS231n and CS224d, they are awesome)

The challenging thing however is the capability to solve real “Business” problems. I quoted business because there is plethora of tutorials using Deep Learning to solve problems like Computer Vision for Auto driven cars or someone More…

Neural networks have been around for a number of decades now and have seen their ups and downs. Recently they’ve proved to be extremely powerful for image recognition problems. Or, rather, a particular type of neural network called a *convolutional neural network* has proved very effective. In this post, I want to build off of the series of posts I wrote about neural networks a few months ago, plus some ideas from my post on digital images, to explain the difference between a convolutional neural network and a classical (is that the right term?) neural network.

First, let me quickly review the idea behind a neural network: We start with a collection of neurons, each of which takes a collection of input values and uses them to calculate a single output value. Then we hook them all together, so that the inputs to each neuron are attached to either the outputs of other neurons or to coordinates/features of a data point that is fed into the network.

When you input a data point into neural network, the outputs of the first level of neurons are calculated, then they feed More…