Search Results for "numpy python 3.12" - Page 4
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Showing 153 open source projects for "numpy python 3.12"
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Chainer
A flexible deep learning framework
Chainer is a Python-based deep learning framework. It provides automatic differentiation APIs based on dynamic computational graphs as well as high-level APIs for neural networks. -
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min(DALL·E)
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
This is a fast, minimal port of Boris Dayma's DALL·E Mini (with mega weights). It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are numpy, requests, pillow and torch. The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Set the device to either cuda or "cpu". Once everything has finished... -
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Scikit-Optimize
Sequential model-based optimization with a `scipy.optimize` interface
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts. The library is built on top of NumPy, SciPy and Scikit-Learn. -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage... -
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Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It... -
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Python Tutorials
Machine Learning Tutorials
Python Tutorials is a large set of educational tutorials focused on Python and related technologies, catering especially to learners who want hands-on examples and clear explanations. Created by an experienced instructor and educator, the repository covers a wide range of programming basics and advanced topics. This includes foundational Python concepts, data processing with libraries like NumPy and pandas, threading and multiprocessing for concurrency, and practical use of libraries such as Matplotlib for data visualization. ... -
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PyArmadillo
linear algebra library for Python
PyArmadillo - streamlined linear algebra library for Python, with emphasis on ease of use. Alternative to NumPy / SciPy. * Main page: https://pyarma.sourceforge.io * Documentation: https://pyarma.sourceforge.io/docs.html * Bug reports: https://pyarma.sourceforge.io/faq.html * Git repo: https://gitlab.com/jason-rumengan/pyarma -
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GIMP ML
AI for GNU Image Manipulation Program
...Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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3DDFA
Fast, accurate and stable 3D dense face alignment
...See requirements.txt, tested on macOS and Linux platforms. The Windows users may refer to FQA for building issues. Note that this repo uses Python3. The major dependencies are PyTorch, numpy, opencv-python and onnxruntime, etc. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. -
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ombakwarna
Spreadsheet based graphical and analysis software
OmbakWarna is a spreadsheet based graphical analysis software utilizing numpy and matplotlib as analysis and graphic tools. Its design is inspired by IgorPro software. It is licensed under GNU General Public License, version 2. This is still works in progress. Some widgets are without functionality. There is no manual yet, but a few videos were uploaded to the files section showing simple tutorials for some plotting capabilities. The videos are also available in youtube:... -
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Open Drug Discovery Toolkit (ODDT)
Modular and comprehensive toolkit for use in cheminformatics
Open Drug Discovery Toolkit (ODDT) is modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc. ODDT is written in Python, and makes extensive use of Numpy/Scipy. You can use any supported toolkit united under common API (for reference see Pybel or Cinfony). All methods and software based on Pybel/Cinfony should be drop-in compatible with ODDT toolkits. In contrast to its predecessors, which were aimed to have minimalistic API, ODDT introduces extended methods and additional handles. ... -
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MCNPydE
MCNP data extraction and display software library
MCNPydE is a Python library for extracting data from MCNP output file. It requires Python, Matplotlib and Numpy. It is a data reduction tool for MCNP output for ease of results analysis and viewing. -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. ... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. ... -
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pyPIDTuneMethods
PID controller design and tuning
pyPIDTuneMethods is a free python-based tool for PID controller design and tuning. pyPIDTuneMethods is based on scipy (numpy, scipy) <http://www.scipy.org/ >, guiqwt <http://code.google.com/p/guiqwt/ >, python control <http://python-control.org/> and pyQt5 <http://www.riverbankcomputing.co.uk >. If you are not familiar with python you can use winpython <http://winpython.sourceforge.net/>. -
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FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same... -
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Python Data Science Tutorials
Common data analysis and machine learning tasks using python
Python Data Science Tutorials is a curated learning repository for common data analysis, machine learning, and natural language processing tasks in Python. It combines external tutorials, courses, reference guides, notebooks, articles, and selected code examples. The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. -
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pyspread
Python spreadsheet application
Pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet. Pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Dependencies + Python (>=2.7, <3.0) + numpy (>=1.1.0) + wxPython (>=2.8.10.1, Unicode version required) + matplotlib (>=1.1.1) + pycairo (>=1.8.8) Optional dependencies + python-gnupg (>=0.3.0, for opening own files without approval) + xlrd (>=0.9.2, for loading Excel® files) + xlwt (>=0.9.2, for saving Excel files, pyspread >=v0.3.0 required) + jedi (>=0.8.0, for tab completion and context help in the entry line, pyspread >=v0.3.0 required) + basemap (>=1.0.7, for the weather example pys file) -
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PySptools
Hyperspectral algorithms for Python
A lightweight hyperspectral imaging library that provides developers with spectral algorithms for the Python programming language. New for v0.14.x: a scikit-learn bridge (alpha and partial). The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. ... -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
Data science solutions, insights, dashboards, machine learning, deployment. We start at 100GB. Vaex is a high-performance Python library for lazy Out-of-Core data frames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive... -
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Tangent
Source-to-source debuggable derivatives in pure Python
...Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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PyVE
PyVE is image analysis and visualization environment
PyVE is image analysis and Visualization Environment focused at clinical use. At the core of it there is a powerful viewer for displaying 3D datasets (MRI, PET, CT) based on VTK. It all comes precompiled allowing painless access to Python (2.x), the ITK toolkit for image analysis, numpy/scipy for numerical calculations, Qt and PyQt4 for the development Graphical User Interfaces. It is what you need for fast prototyping and development of more complex projects. -
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Numerical Python
A package for scientific computing with Python
NEWS: NumPy 1.11.2 is the last release that will be made on sourceforge. Wheels for Windows, Mac, and Linux as well as archived source distributions can be found on PyPI. Numerical Python adds a fast and sophisticated array facility to the Python language. NumPy is the most recent and most actively supported package. Numarray and Numeric are no longer supported.