Sequence processing with neural nets
The DyNet toolkit introduces a dynamic declaration paradigm for neural networks, constructing computational graphs on-the-fly during execution. This approach simplifies the implementation of
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Generative Adversarial Networks or GAN are a type of deep generative models which are capable of generating new data samples from the underlying probability distribution of training data
When neural nets try to name things, the results can be indisputably weird. Every once in a while, I come across an arena where the human-invented names are every bit as strange as those a neural net can come up with. Often, scientists are to blame. Species of
Adding gradient noise improves the learning of very deep networks by stochastically perturbing the optimization process
A neural processing unit (NPU) is a specialized computer microprocessor designed to mimic the processing function of the human brain
A solid neural network training guide is worth more than any single framework tutorial, because the core concepts—weight initialisation, loss functions
I started working though the second chapter of McCaffrey’s book Neural Networks Using C# Succinctly to see if I could write the examples using F#. McCaffrey’s code is tough to read though because of its emphasis on loops and global mutable variables. I read though his description and this is how <I think> the Perceptron
You've probably built a neural network using PyTorch or Keras. You called model.fit(), watched the loss go down, and celebrated. But do you actually know what