What if in our attempt to build artificial intelligence we don’t simulate neurons in code and mimic neural networks in Python, but instead build actual physical neurons connected by physical synapses ...

The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations. Regularization is a ...

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Neural network dropout is a technique that can be used during training. It is designed to reduce the likelihood of model overfitting. You can think of a neural network as a complex math equation that ...

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Artificial Intelligence—or, if you prefer, Machine Learning—is today’s hot buzzword. Unlike many buzzwords have come before it, though, this stuff isn’t vaporware dreams—it’s real, it’s here already, ...