Pinned Repositories
3D-point-capsule-networks
3D Point Capsule Networks
A-Persistence-Based-Approach-to-Automatic-Detection-of-Line-Segments-in-Images
A Persistence-Based Approach to Automatic Detection of Line Segments in Images - Paper: Kurlin, Vitaliy, and Muszynski Grzegorz, "A Persistence-Based Approach to Automatic Detection of Line Segments in Images", International Workshop on Computational Topology in Image Context. Springer, Cham, 2019.
AR-Detection-Method-TDA-ML
Atmospheric River Pattern Detection Method (Topological Data Analysis + Machine Learning)
Atmospheric-Blocking-Classification-Persistent-Homology-2D-Convolutoional-Neural-Network
Atmospheric Blocking Classification: Persistent Homology + 2D Convolutoional Neural Network
emulation
Active learning for emulation of a cloud parcel model
new_project_mid_lat
All code for ICPR 2020 paper on atmospheric blocking detection (a binary classification + an localisation (regression task)) using CNNs based on VGG archs.
ppe_icon_model
This is a cloned ICON repository that is used for physics perturbed ensemble experiments.
project_causal_climate_dynamics
Causal discovery of drivers of the summer Himalayan precipitation
rl-reliability-metrics
The RL Reliability Metrics library provides a set of metrics for measuring the reliability of reinforcement learning (RL) algorithms, as well as statistical tools for comparing algorithms and for computing confidence intervals on these metrics.
xarray-tutorial
Xarray Tutorials
muszyna25's Repositories
muszyna25/Atmospheric-Blocking-Classification-Persistent-Homology-2D-Convolutoional-Neural-Network
Atmospheric Blocking Classification: Persistent Homology + 2D Convolutoional Neural Network
muszyna25/3D-point-capsule-networks
3D Point Capsule Networks
muszyna25/A-Persistence-Based-Approach-to-Automatic-Detection-of-Line-Segments-in-Images
A Persistence-Based Approach to Automatic Detection of Line Segments in Images - Paper: Kurlin, Vitaliy, and Muszynski Grzegorz, "A Persistence-Based Approach to Automatic Detection of Line Segments in Images", International Workshop on Computational Topology in Image Context. Springer, Cham, 2019.
muszyna25/astronomical_classification
Python + Tensorflow
muszyna25/CFF_Dijkstra_Algorithm
Application of Dijkstra algorithm for finding optimal route between cities, based on the national railway of Switzerland.
muszyna25/Kohonen_Network
Advanced Implementation of Kohonen Network
muszyna25/Acc_PhysicalTracker
Android project - course Android Application Development.
muszyna25/BattleShip--
GUI of the game battleship in Java
muszyna25/Causal-Discovery
Summary of causal structure learning methods
muszyna25/convnet-drawer
Python script for illustrating Convolutional Neural Networks (CNN) using Keras-like model definitions
muszyna25/dl4sci-scaling-tutorial
Deep Learning Scaling tutorial material for the Deep Learning for Science School at Berkeley Lab
muszyna25/GIoU-loss-for-RetinaNet
Change smooth L1 loss to GIoU loss for RetinaNet
muszyna25/gpu-for-science-day-july-2019
Hacking Competition Code for NERSC's GPU for Science Day, July 2019
muszyna25/gym
A toolkit for developing and comparing reinforcement learning algorithms.
muszyna25/keras-rl
Deep Reinforcement Learning for Keras.
muszyna25/Python-Practical-Application-on-Climate-Variability-Studies
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
muszyna25/pytorch_stacked_hourglass
Pytorch implementation of "Stacked Hourglass Networks for Human Pose Estimation"
muszyna25/Reed_Muller
Implementation of Reed_Muller algorithm.
muszyna25/Regression-Classification-Keras
Demonstration of how to use the Keras library in Deep Neural Networks to solve Regression as well as Classification Problems.
muszyna25/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
muszyna25/reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
muszyna25/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
muszyna25/ShopView
View of card description
muszyna25/TECA
TECA, theToolkit for Extreme Climate Analysis, contains a collection of climate anlysis algorithms targetted at extreme event detection and analysis.
muszyna25/Topological-Methods-for-Climate-Data
Working prototype of topological & machine learning method for AGU book chapter, Wiley.