All Projects → EricDarve → numerical_linear_algebra

EricDarve / numerical_linear_algebra

Licence: other
Julia code for the book Numerical Linear Algebra

Programming Languages

Jupyter Notebook
11667 projects
julia
2034 projects

Projects that are alternatives of or similar to numerical linear algebra

svd-image-compression-demo
Demonstration of low rank matrix approximations via singular value decomposition
Stars: ✭ 23 (-46.51%)
Mutual labels:  singular-value-decomposition
Digital-Image-Watermarking
Digital Image Watermarking Method Based on Hybrid DWT-HD-SVD Technique: Attacks, PSNR, SSIM, NC
Stars: ✭ 37 (-13.95%)
Mutual labels:  singular-value-decomposition
analisis-numerico-computo-cientifico
Análisis numérico y cómputo científico
Stars: ✭ 42 (-2.33%)
Mutual labels:  matrix-computations
TotalLeastSquares.jl
Solve many kinds of least-squares and matrix-recovery problems
Stars: ✭ 23 (-46.51%)
Mutual labels:  singular-value-decomposition
machine-learning
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…
Stars: ✭ 91 (+111.63%)
Mutual labels:  singular-value-decomposition
Recommendation.jl
Building recommender systems in Julia
Stars: ✭ 42 (-2.33%)
Mutual labels:  singular-value-decomposition
math105A
Numerical analysis course in Python
Stars: ✭ 20 (-53.49%)
Mutual labels:  qr-decomposition
Recommender-Systems
Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python
Stars: ✭ 46 (+6.98%)
Mutual labels:  singular-value-decomposition
iterative-grabcut
This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.
Stars: ✭ 35 (-18.6%)
Mutual labels:  iterative-methods
primme
PReconditioned Iterative MultiMethod Eigensolver for solving symmetric/Hermitian eigenvalue problems and singular value problems
Stars: ✭ 98 (+127.91%)
Mutual labels:  singular-value-decomposition
imgsvd
Shiny App for Image Compression via SVD
Stars: ✭ 22 (-48.84%)
Mutual labels:  singular-value-decomposition

Julia code for the book Numerical Linear Algebra

Build Status

To use this code, please download and install Julia from julialang.org/downloads.

Julia installation

Step 1: if you have installed Julia on your computer, you can download this repository using the green Clone or download button above. Once you have Julia installed, you can run all the Julia codes in this repository (files with extension .jl).

To run the Julia notebooks (files with extension .ipynb), you need to install IJulia and Jupyter.

Step 2: IJulia is a Julia package. To install it, start the Julia application. At the julia> prompt, type:

using Pkg
Pkg.add("IJulia")

See IJulia for more detailed instructions.

Step 3: Install Jupyter following these instructions.

Step 4: type jupyter notebook in a terminal window. Make sure you are in the directory containing your notebook. You should be able to open the notebook from the jupyter window inside your web browser.

You can run the example notebook Demo.ipynb contained in this repository. After opening Demo.ipynb, wait for the kernel to be ready (check the top right corner of the window), then click on Cell -> Run All to update the plot. You may have to change the Julia kernel to match the one that is installed. Click on Kernel -> change kernel for this.

Read the notebook. You should see a plot at the end.

You are now ready to go!

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].