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adaptive-filter-for-acoustic-signal-segmentation

An innovative tool based on gradient-based optimization for segmenting acoustic signals. Features binary and ternary segmentation, time series analysis, and links to the published research in Nature Communications. Open for contributions.

bond-investment-analysis-and-ytm-calculation

Comprehensive Jupyter notebook guide for calculating Yield to Maturity (YTM) and analyzing bond investment returns and risks, using Italian BTP bonds as a case study. Ideal for finance students and investors seeking a deeper understanding of bond market dynamics.

deepphysicssim

DeepPhysicsSim: 3D Fluid Flow and Temperature Simulation with Deep Learning

dual-disco

laser_rl

lpbf-dual-laser-signal-recovery

Toolkit for separating and analyzing signals from dual-laser LPBF processes. Enhances print quality and process insight through noise reduction, signal recovery, and visualization.

lpbf-melting-regimes-detection

The code provides a data analysis pipeline for detecting melting regimes in Laser Powder Bed Fusion (LPBF) using optical reflection and emission data.

lpbf-optical-data-analysis

A comprehensive pipeline of Jupyter notebooks for the analysis and visualization of optical data collected from Laser Powder Bed Fusion (LPBF) experiments.

lpbf-smartam-optical-data

ntds_2016

Material for the EPFL master course "A Network Tour of Data Science"

rl-custom-env-experiments

A collection of reinforcement learning experiments utilizing custom environments to explore and benchmark advanced RL algorithms like SAC, VPG, and DDPG. The repository includes well-structured and documented Python scripts for training RL agents in complex simulation environments, with a focus on waveform generation and control tasks.

spinningup

An educational resource to help anyone learn deep reinforcement learning.

statistical-significance-in-portfolio-management

Exploring the statistical basis for validating portfolio management skill through hypothesis testing, Z-tests, and confidence intervals.

temperature-sampling-frequency-analysis

Temperature-Sampling-Frequency-Analysis: Analyze high-frequency temperature data from Rimini to determine the minimum sampling frequency required to avoid aliasing and accurately capture temperature variations. Includes data, Jupyter Notebook, and theoretical background.

wmc

Embedded machine learning for wood moisture classification