Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
It has been proposed by E. Gelenbe in 1989. A Random Neural Network is a compose of Random Neurons and Spikes that circulates through the network. According to this model, each neuron has a positive ...
How-To Geek on MSN
Generate realistic test data in Python fast. No dataset required
Learn the NumPy trick for generating synthetic data that actually behaves like real data.
These Jupyter notebooks provide interactive Python tutorials for development with Coral. You can download these files and run them on a local Jupyter notebook, but ...
Abstract: Pseudo-random binary sequence (PRBS) has been implemented through DC-DC converters for internal impedance identification and shown to be faster and easier to implement than analogue multiple ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results