Last week, we built our Deep Learning foundation, learning about perceptrons and the backprop algorithm. This week, we are learning about optimization methods. We will start with Stochastic Gradient ...

In 2006–2011, “deep learning” was popular, but “deep learning” mostly meant stacking unsupervised learning algorithms on top of each other in order to define complicated features for ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

As deep learning algorithms become more sophisticated, their applications have expanded widely, ... which is part of the broader Introduction to Generative AI Learning Path Specialization series.

Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the ...

Ability to Handle Complex Data: Deep learning is particularly well-suited to analyzing unstructured data, such as images, video, and text, which traditional algorithms struggle with.

Algorithms and deep learning: the best of both worlds. Veličković was in many ways the person who kickstarted the algorithmic reasoning direction in DeepMind.

Algorithmia today is adding 15 deep-learning algorithms to its marketplace of roughly 2,000 callable APIs of all kinds, said Diego Oppenheimer, Algorithmia’s founder and CEO.

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

Deep learning is good at finding patterns in reams of data, but can't explain how they're connected. Turing Award winner Yoshua Bengio wants to change that.