Projects with this topic
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Materials on various sections in Computer Science (CS).
🎓 с с++ shell Bash makefile Git GitLab github SQL computer sci... PowerShell Mobile Devel... Android iOS devops game develop... Web Development JavaScript Java Python Docker HTML CSS TypeScript C++ Rust game Go C C# python3 nodejs golang Django Node.js MySQL Kotlin Windows PostgreSQL Flutter machine lear... js Ruby Qt Markdown R Swift cybersecurity Cyber Security bioinformatics deep learning big data NumPy pandas matplotlib scikit-learn scipy development softwareUpdated -
Fundamental theory and practice in Data Science (DS).
🧮 data analysis AI ML DL machine lear... deep learning data science data-enginee... artificial i... data-science data preproc... Python C C++ NumPy pandas mathematics Algorithm algorithms Data Enginee... big data scipy scikit-learn xgboost lightgbm catboost TensorFlow keras PyTorch matplotlib seaborn plotly nltk opencv dask linear-algebra calculus probability statistics Discrete Mat... RUpdated -
Miscellaneous Python tools (monorepo).
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Provides fractional delay filter design and evaluation routines for Numpy arrays
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ScaLAPACK for Python (scalapy) provides a high-level Python interface to distributed dense linear algebra using MPI, BLACS, and ScaLAPACK. It enables efficient computation on large matrices using a block-cyclic distribution, while exposing a NumPy-like API based on the DistributedMatrix abstraction.
This fork modernizes and stabilizes the original scalapy project with: • Support for Python 3.x on modern Linux systems • Clean MPI + ScaLAPACK detection (OpenMPI + Netlib ScaLAPACK) • Reproducible builds using a known-good NumPy / Cython / mpi4py stack • Documentation, examples, and automated tests • High-level wrappers for SVD, QR, LU, Cholesky, eigenproblems, and DMD workflows • Efficient distributed transposes via the new p?lapv2 wrapper
The project makes it possible to write dense parallel linear-algebra pipelines in Python while executing BLAS/PBLAS/ScaLAPACK kernels across an MPI communicator.
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Practical tasks on Deep Learning (DL) and Neural Networks (NN).
🤖 Python machine lear... deep learning NumPy matplotlib pandas AI mathematics computer vision natural lang... speech proce... PyTorch scikit-learn artificial i... ML DL big data data analysis scipy keras TensorFlow seaborn plotly nltk opencv dask Deep Nerual ... programming openml google colab google colla... google drive computer sci... CSV API python3 jupyter jupyter note... Anaconda Bash shell LaTeX MarkdownUpdated -
Python libraries and utilities for custom measurement and control devices.
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dantro is a python package for handling, transforming, and visualizing hierarchically organized data.
Integrated into data-intensive projects, it supplies an easy way to define a customizable, configuration-based data processing pipeline. See utopya for an example.
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MCODAC is a Fortran library for the numerical evaluation of fiber composite damage. The library contains analysis methods specifically tailored to fiber composites, from micromechanical homogenization approaches to macroscopic fatigue models of orthotropic multilayer composites.
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Beos is a legacy Fortran tool used to calculate the static and dynamic buckling behavior of flat/curved fiber composite structures.
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Boxbeam is a legacy Fortran tool translated to python. It calculates effective beam properties of composite cross sections comprised of rod-like elements.
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This repo will have all resources, labs, data which I use/d on Kaggle Network
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image(hight, width) -> parts(224,224) «Я могу предстать [перед судом], и я могу это предотвратить»
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The python-native Latina Conservatory version of the SMS (Spectral Modeling Synthesis) tools developed by Xavier Serra in his Phd thesis in 1989.
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Vintage / Second-hand Marketplace Item Identifier & Price Comparator
Basic Idea: User takes photos of second-hand items (books, clothes, electronics) – especially relevant to students/people buying second-hand in Ireland. ML model recognises item type + approximate category + suggests price range (using scraped data from online marketplaces like DoneDeal, DoneDeal.ie, etc.). Backend stores user offers/requests; allows search, history, notifications for good deals.
Datasets / Data sources: For generic objects: use general vision datasets such as ImageNet – wide variety of everyday objects & categories. For clothes / fashion: Fashion‑MNIST for a start (though low-resolution, but good prototyping). For more specialized classes, get good coverage from public datasets like ImageNet.
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L'Informatica Musicale Veramente Giusta. Informatica Musicale per le persone dubbiose.
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