sgh14

ctlearn

Deep Learning for IACT Event Reconstruction

autoencoders-and-serial-autoencoders

CTGANs

Generative adversarial networks for conditional generation of images from Cherenkov telescopes (IACTs).

ctlearn

Deep Learning for IACT Event Reconstruction

deep-diffusion-maps-and-deep-diffusion-autoencoders

Implementation of Diffusion Maps using neural networks and combination of Diffusion Maps with Deep Autoencoders.

deep-quantum-sensing

Deep learning to make predictions from quantum sensor measurements

diffusion-maps

Diffusion maps algorithm for manifold learning (dimensionality reduction) with Python and optimized with Numba

ecosystem-simulation

Simulation of competitive ecological communities of sessile individuals

MNIST_GANs

Generative adversarial networks for conditional generation of images from MNIST dataset

quantum-sensor-simulation

Simulate the interaction between two systems of spins (sensors and targets)

sgh14/quantum-sensor-simulation

Simulate the interaction between two systems of spins (sensors and targets)

sgh14/autoencoders-and-serial-autoencoders

sgh14/CTGANs

Generative adversarial networks for conditional generation of images from Cherenkov telescopes (IACTs).

sgh14/ctlearn

Deep Learning for IACT Event Reconstruction

sgh14/deep-diffusion-maps-and-deep-diffusion-autoencoders

Implementation of Diffusion Maps using neural networks and combination of Diffusion Maps with Deep Autoencoders.

sgh14/deep-quantum-sensing

Deep learning to make predictions from quantum sensor measurements

sgh14/diffusion-maps

Diffusion maps algorithm for manifold learning (dimensionality reduction) with Python and optimized with Numba

sgh14/ecosystem-simulation

Simulation of competitive ecological communities of sessile individuals

sgh14/MNIST_GANs

Generative adversarial networks for conditional generation of images from MNIST dataset