**The Numerical Tours of Data Sciences**

The Numerical Tours of Data Sciences, by **Gabriel Peyré**, gather Matlab, Python, Julia and R experiments to explore modern mathematical data sciences. They cover data sciences in a broad sense, including imaging, machine learning, computer vision and computer graphics. It showcases application of numerical and mathematical methods such as convex optimization, PDEs, optimal transport, inverse problems, sparsity, etc. The tours are complemented by slides of courses detailing the theory and the algorithms.

**Numerical Tours now in R**

Link - 35 R tours available

Posted by Gabriel Peyré on February 26, 2018

**Numerical Tours on Machine Learning**

Link - 4 new Matlab and Python tours

Posted by Gabriel Peyré on August 11, 2017

**Numerical Tours now in Julia**

Link - 30 Julia tours available

Posted by Gabriel Peyré on August 5, 2017

**Numerical Tours now in Python**

Link - 30 Python tours available

Posted by Gabriel Peyré on September 17, 2016

**New Python Tours**

Optimization by Laurent Condat

Posted by Gabriel Peyré on June 14, 2016

**Numerical Tours now in R**

**OFFICIAL WEBPAGE**

The R tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

**Basics**

- Introduction to Image Processing
- Le traitement numérique des images
- Image Approximation with Fourier and Wavelets

Wavelets

**Approximation, Coding and Compression**

**Denoising**

- Linear Image Denoising
- Wavelet Denoising
- Wavelet Block Thresholding
- Non Local Means
- Rank Filters for Image Processing

**Inverse Problems**

- Image Deconvolution using Variational Method
- Inpainting using Sparse Regularization
- Performance of Sparse Recovery Using L1 Minimization

**Optimization**

- Gradient Descent Methods
- Gradient Descent (by Laurent Condat)
- Forward-Backward Splitting
- Forward-Backward method on the dual problem
- Douglas Rachford Proximal Splitting

* Chambolle-Pock Primal-Dual Splitting Algorithm

**Machine Learning**

- PCA, Nearest-Neighbors and Clustering
- Linear Regression and Kernel Methods
- Logistic Classification
- Stochastic Gradient descent

**Shapes**

- Edge detection
- Active Contours using Parameteric Curves
- Active Contours using Level Sets
- Manifold Learning with Isomap

**Audio Processing**

**Computer Graphics**

**Mesh Parameterization and Deformation**

**Geodesic Processing**

**Optimal Transport**

**OFFICIAL WEBPAGE**

hese are the Python tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

**Basics**

- Introduction to Image Processing
- Le traitement numérique des images
- Image Approximation with Fourier and Wavelets

**Wavelets**

**Approximation, Coding and Compression**

**Denoising**

- Linear Image Denoising
- Wavelet Denoising
- Wavelet Block Thresholding
- Non Local Means
- Rank Filters for Image Processing
- Stein Unbiased Risk Estimator

**Inverse Problems**

- Image Deconvolution using Variational Method
- Inpainting using Sparse Regularization
- Performance of Sparse Recovery Using L1 Minimization

**Optimization**

- Gradient Descent Methods
- Gradient Descent (by Laurent Condat)
- Forward-Backward Splitting (by Laurent Condat)
- Forward-Backward method on the dual problem (by Laurent Condat)
- Douglas Rachford Proximal Splitting (by Laurent Condat)
- Chambolle-Pock Primal-Dual Splitting Algorithm (by Laurent Condat)

**Shapes**

- Edge Detection
- Active Contours using Parameteric Curves
- Active Contours using Level Sets
- Manifold Learning with Isomap

**Audio Processing**

**Computer Graphics**

**Mesh Parameterization and Deformation**

**Geodesic Processing**

**Optimal Transport**

**Machine Learning**

**The Numerical Tours of Data Sciences**

The Numerical Tours of Data Sciences, by **Gabriel Peyré**, gather Matlab, Python, Julia and R experiments to explore modern mathematical data sciences. They cover data sciences in a broad sense, including imaging, machine learning, computer vision and computer graphics. It showcases application of numerical and mathematical methods such as convex optimization, PDEs, optimal transport, inverse problems, sparsity, etc. The tours are complemented by slides of courses detailing the theory and the algorithms.

**Numerical Tours now in R**

Link - 35 R tours available

Posted by Gabriel Peyré on February 26, 2018

**Numerical Tours on Machine Learning**

Link - 4 new Matlab and Python tours

Posted by Gabriel Peyré on August 11, 2017

**Numerical Tours now in Julia**

Link - 30 Julia tours available

Posted by Gabriel Peyré on August 5, 2017

**Numerical Tours now in Python**

Link - 30 Python tours available

Posted by Gabriel Peyré on September 17, 2016

**New Python Tours**

Optimization by Laurent Condat

Posted by Gabriel Peyré on June 14, 2016

**Numerical Tours now in R**

**OFFICIAL WEBPAGE**

The R tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

**Basics**

- Introduction to Image Processing
- Le traitement numérique des images
- Image Approximation with Fourier and Wavelets

Wavelets

**Approximation, Coding and Compression**

**Denoising**

- Linear Image Denoising
- Wavelet Denoising
- Wavelet Block Thresholding
- Non Local Means
- Rank Filters for Image Processing

**Inverse Problems**

- Image Deconvolution using Variational Method
- Inpainting using Sparse Regularization
- Performance of Sparse Recovery Using L1 Minimization

**Optimization**

- Gradient Descent Methods
- Gradient Descent (by Laurent Condat)
- Forward-Backward Splitting
- Forward-Backward method on the dual problem
- Douglas Rachford Proximal Splitting

* Chambolle-Pock Primal-Dual Splitting Algorithm

**Machine Learning**

- PCA, Nearest-Neighbors and Clustering
- Linear Regression and Kernel Methods
- Logistic Classification
- Stochastic Gradient descent

**Shapes**

- Edge detection
- Active Contours using Parameteric Curves
- Active Contours using Level Sets
- Manifold Learning with Isomap

**Audio Processing**

**Computer Graphics**

**Mesh Parameterization and Deformation**

**Geodesic Processing**

**Optimal Transport**

**OFFICIAL WEBPAGE**

hese are the Python tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

**Basics**

- Introduction to Image Processing
- Le traitement numérique des images
- Image Approximation with Fourier and Wavelets

**Wavelets**

**Approximation, Coding and Compression**

**Denoising**

- Linear Image Denoising
- Wavelet Denoising
- Wavelet Block Thresholding
- Non Local Means
- Rank Filters for Image Processing
- Stein Unbiased Risk Estimator

**Inverse Problems**

- Image Deconvolution using Variational Method
- Inpainting using Sparse Regularization
- Performance of Sparse Recovery Using L1 Minimization

**Optimization**

- Gradient Descent Methods
- Gradient Descent (by Laurent Condat)
- Forward-Backward Splitting (by Laurent Condat)
- Forward-Backward method on the dual problem (by Laurent Condat)
- Douglas Rachford Proximal Splitting (by Laurent Condat)
- Chambolle-Pock Primal-Dual Splitting Algorithm (by Laurent Condat)

**Shapes**

- Edge Detection
- Active Contours using Parameteric Curves
- Active Contours using Level Sets
- Manifold Learning with Isomap

**Audio Processing**

**Computer Graphics**

**Mesh Parameterization and Deformation**

**Geodesic Processing**

**Optimal Transport**

**Machine Learning**