These guys have shown how evolutionary computing can match the performance of deep-learning machines at the emblematic task that first powered them to fame in 2013—the ability to outperform ...

Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs. The more layers in the neural ...

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 ...

Jordan Miller discusses the evolution of the Clojure ecosystem, ... Deep Learning Pioneer Geoffrey Hinton Publishes New Deep Learning Algorithm This item in japanese Jan 10, 2023 ...

8 practical examples of deep learning. Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling?

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

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.

Then I’ll discuss 14 of the most commonly used machine learning and deep learning algorithms, and explain how those algorithms relate to the creation of models for prediction, classification ...

A deep reinforcement learning algorithm can solve the Rubik's Cube puzzle in a fraction of a second. The work is a step toward making AI systems that can think, reason, plan and make decisions.