Numerical Bric-a-Brac
Collections of numerical techniques with Python and standard computational packages (Numpy, SciPy, Numba, Matplotlib ...).
Differential Equations
Differential Equations describe many physical systems
Notebooks
GitHub |
Implementation of Baraff's "Large steps in cloth simulation." |
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Analytic solution to damped spring |
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Diffusion problem |
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Laplace inpainting to restore an image |
Optimizations
Those notebooks are the core algorithms for supervised and unsupervised learning in Machine learning.
Machine Learning Notebooks
Linear Algebra
Standard linear algebra algorithms
Notebooks
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Inverse kinematics using least square methods to solve the ill-posed problem |
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Interpolation with Radial Basis Functions (RBFs) |
Graph Theory
Part of discrete mathematics, graph theory is the study of graphs, a structures used to model pairwise relations between objects. Graph theory includes problems such as the travelling salesman problem and the spectral analysis of graph.
Notebooks
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List matrices describing a finite graph. |
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Greedy algorithm for graph coloring. |
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Find the shortest paths between nodes in a graph. |
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Spectral Graph Thoery - PLACEHOLDER (WIP). |
Neural Network
Artificial neural networks are composed of artificial neurons organized into layers. Neural networks are employed for regression analysis (function approximation) and classification problems.
Notebooks
Stencil Codes
Stencil codes are at the heart of many numerical solvers and physical simulation codes. They are of particular interest in scientific computing research for solving partial differential equations, image processing, cellular automata, etc. The Finite Difference Method (FDM) is closely related to stencil codes.
Notebooks
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Diffusion problem |
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Laplace inpainting to restore an image |
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Image processing with convolution kernels |
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Cellular automata with Conway's game of life rules |
Dimensionality Reduction
Dimensionality reduction is the process of converting data from a high-dimensional space to a low-dimensional space (subspace). A lower dimension representation aids in comprehending meaningful properties (such as latent variables), compressing the data, and removing noise from the original data.
Notebooks
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Optimal transformation of a point cloud with PCA |
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Image compression with Singular Value Decomposition |
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Autoencoder on Fashion MNIST |
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Eigenfaces for face recognition on Olivetti faces dataset |
Miscellaneous
Random notebooks about dynamic programming and monte carlo methods
Notebooks
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Example of ML pipeline with Snakemake |
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Dynamic programming Examples |
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Examples of Monte Carlo integrations |
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Skeletal Subspace Deformation |
GitHub |
Monte Carlo Path Tracing |
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Generate authors names from the Collection of Poems from Poetry Foundation |