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A deep learning algorithm requires tuning of following possible set of parameters (also known as hyperparameters): $\alpha$ - learning rate - the most important hyperparameter to tune learning rate decay - probably second in importance mini-batch size - as important as learning rate decacy mini-batch iterations number of layers $\beta$ ... read more

Commuters of Greater Toronto Area (GTA) are plagued by the question of whether to take 407 in their daily commute, or not. People come up with different heuristics from listening to traffic news, anticipating good/bad weather, guessing traffic conditions on a particular day of the week to avoiding rush hours, ... read more

Gradient descent is commonly used search algorithm in machine learning. We use it to learn parameters of prediction model. One of the problems with straightforward gradient descent is that it oscillates too much on vertical axis (as show in above image). This oscillations slows down the convergence to minimum (the ... read more

Deep learning algorithm is an artificial neural network (ANN) which has many hidden layers. Neural network is inspired by how our brain works. Each of its node acts as a neuron - it takes input from previous layer and feeds output to nodes in the next layer of neural network. ... read more

In short, very. Machine Learning (ML) is at the height of Gartner’s hype cycle, and we see the corresponding boom in the job market. AI will bring the next industrial revolution, claimed at TED. Others claim there is a ML startup for every job that doesn’t have ML. Tall... read more