Deep neural networks are very powerful models, in theory able to approximate any function. In practice things are a little different. Oddly, a neural network tends to generalize better the larger it is, often to the point of having more parameters than there are data points in the training set.... [Read More]

When you peek under the hood of a deep learning library, things will look pretty complex. That’s because they use a lot of abstractions to handle a wide variety of cases. But the core code of a Deep Learning library is actually not that complex at all. In fact, you’d... [Read More]

Gradient descent is perhaps the simplest learning optimization algorithm that exists. In Deep Learning, it’s the foundational learning algorithm upon which modern learning algorithms are developed. [Read More]

You may have wondered why there are so many different algorithms for machine learning - after all, would it not be sufficient with one algorithm? A famous result is the no free lunch theorem, which tells us that no one algorithm will be optimal for every problem. [Read More]

A pain free recipe [Read More]

What’s the fuzz about? [Read More]