cristina-v-melnic
Trained physicist, graduated MSc Computational Sciences at FU Berlin. Currently exploring computational neuroscience, data science and machine learning
Berlin, Germany
Pinned Repositories
cristina-v-melnic
Config files for my GitHub profile.
force-inference
Application to find parameters of inter-particle forces from stochastic trajectories using Bayesian inference.
handson-ml2-fork
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
neural-orientation-tuning
Model of a single orientation-selective neuron receiving inputs from afferents with different preferred orientations. The project shows what properties the postsynaptic neuron has and how they depend on the input. It aims to investigate two types of connectivity of neurons in the primary visual cortex, i.e., weight-based and number-based.
pattern-formation
Modelling pattern formation by numerical integration of systems of coupled convection-diffusion differential equations.
stokes-dg-experiments
Script that runs experiments on the implemented H(div)-conforming elements for Stokes equations in the finite element package ParMooN.
stokes-dg-figures
Analysis of simulation data obtained with the ParMooN finite element package.
cristina-v-melnic's Repositories
cristina-v-melnic/pattern-formation
Modelling pattern formation by numerical integration of systems of coupled convection-diffusion differential equations.
cristina-v-melnic/cristina-v-melnic
Config files for my GitHub profile.
cristina-v-melnic/force-inference
Application to find parameters of inter-particle forces from stochastic trajectories using Bayesian inference.
cristina-v-melnic/handson-ml2-fork
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
cristina-v-melnic/neural-orientation-tuning
Model of a single orientation-selective neuron receiving inputs from afferents with different preferred orientations. The project shows what properties the postsynaptic neuron has and how they depend on the input. It aims to investigate two types of connectivity of neurons in the primary visual cortex, i.e., weight-based and number-based.
cristina-v-melnic/stokes-dg-experiments
Script that runs experiments on the implemented H(div)-conforming elements for Stokes equations in the finite element package ParMooN.
cristina-v-melnic/stokes-dg-figures
Analysis of simulation data obtained with the ParMooN finite element package.