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

Transformers are a neural network (NN) architecture, or model, that excels at processing sequential data by weighing the ...

What if a model could forget without losing its mind?” That question now has a technical foothold, thanks to new research ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of ...

We take things up a notch in this other notebook to visualize how neural networks bend space to make the data linearly separable in the final layer: For people who like to understand how things work ...

Abstract: In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage ...

ABSTRACT: An algorithm is being developed to conduct a computational experiment to study the dynamics of random processes in an asymmetric Markov chain with eight discrete states and continuous time.

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...