We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

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 ...

With Python and NumPy getting lots of exposure lately, I'll show how to use those tools to build a simple feed-forward neural network. Over the past few months, the use of the Python programming ...

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 ...

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, ...

Overview Books provide a deeper understanding of AI concepts beyond running code or tutorials.Hands-on examples and practical ...

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 ...

Researchers at MIT say they’ve developed an algorithm capable of processing magnetic resonance images in less than a second, in what could be a crucial development for the healthcare industry.

Many people have built brain-like neural networks that can learn on their own, but they're typically using plain old silicon to do it. Wouldn't it be better if the chips themselves were brain-like? A ...