Discover the role of optimizers in deep learning! Learn how algorithms like SGD, Adam, and RMSprop help neural networks train ...

Commonly, ML algorithms could be divided into four categories as follows: 1) supervised learning, 2) unsupervised learning, 3) semi-supervised learning, and 4) reinforcement learning. Some of the most ...

Using a range of real datasets and basic Python libraries for data manipulation, vector/matrix algebra, and automatic differentiation students will code up - from scratch - fundamental optimization ...

In business, much to the data scientist’s pleasure, so much of optimization is in finding an even narrower local maximum or minimum. That’s a key reason why deep learning systems are of such ...

So where are we at, especially with the emerging opportunities for deep learning on the horizon? Current state-of-the-art leadership for deep learning in R is provided by a whole slew of “bolt on” ...

A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms—and might also explain how human brains learn.

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used ...

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.