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PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.
neural-network autograd gpu numpy deep-learning tensordata-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines
IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.
machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy kerasxarray - N-D labeled arrays and datasets in Python
xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.
scientific-computing netcdf numpy data-science pandas dataframes data-analysis pydataPythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.
scikit-learn numpy jupyter-notebook matplotlib pandasfrom-python-to-numpy - An open-access book on numpy vectorization techniques, Nicolas P
There are already a fair number of books about NumPy (see bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there is room for a different approach concentrating on the migration from Python to NumPy through vectorization. There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process.
numpy vectorization book open-access cc-by-nc-sanumpy-100 - 100 numpy exercises (100% complete)
This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation. I've also created some problems myself to reach the 100 limit. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. This work is licensed under the MIT license.
numpy binder notebook exercisesnumjs - Like NumPy, in JavaScript
Besides its obvious scientific uses, NumJs can also be used as an efficient multi-dimensional container of generic data. NumJs is licensed under the MIT license, enabling reuse with almost no restrictions.
linear-algebra ndarray nodejs array multi multidimensional dimension higher image volume webgl tensor matrix linear algebra science numerical computing stride shape numpychainer - A flexible framework of neural networks for deep learning
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.
deep-learning neural-networks machine-learning gpu cuda cudnn numpy cupy chainer neural-networkmlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian
This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.
machine-learning data-analysis data-science pandas algorithms numpy scipy matplotlib seaborn plotly scikit-learn kaggle-inclass vowpal-wabbit ipynb docker mathxtensor - C++ tensors with broadcasting and lazy computing
Multi-dimensional arrays with broadcasting and lazy computing. xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.
c-plus-plus-14 numpy multidimensional-arrays tensorsnumpy_exercises - Numpy exercises.
In numerical computing in python, NumPy is essential. I'm writing simple (a few lines for each problem) but hopefully helpful exercises based on each of numpy's functions. The outline will be as follows.
numpy numpy-exercisescupy - NumPy-like API accelerated with CUDA
CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface. For detailed instructions on installing CuPy, see the installation guide.
cuda cudnn cublas cusolver nccl numpy cupy curand cusparsepython-cheat-sheet - Python Cheat Sheet NumPy, Matplotlib
This rep is a growing list of Python cheat sheets, tailored for Data Science. If you want to install a package individually, go into the corresponding <package-name>.md file for instructions on how to install.
numpy python-cheat-sheets data-science pandas scikit-learntutorials - 机器学习相关教程
我是 周沫凡, 莫烦Python 只是谐音, 我喜欢制作, 分享所学的东西, 所以你能在这里找到很多有用的东西, 少走弯路. 你能在这里找到关于我的所有东西. 这些 tutorial 都是我用业余时间写出来, 录成视频, 如果你觉得它对你很有帮助, 请你也分享给需要学习的朋友们. 如果你看好我的经验分享, 也请考虑适当的 赞助打赏, 让我能继续分享更好的内容给大家.
machine-learning neural-network tensorflow sklearn theano threading multiprocessing numpy100-pandas-puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power. Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. Many of the excerises here are straightforward in that the solutions require no more than a few lines of code (in pandas or NumPy - don't go using pure Python!). Choosing the right methods and following best practices is the underlying goal.
pandas numpy data-analysisnumpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. NumPyro is a small probabilistic programming library that provides a NumPy backend for Pyro. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU. This is an alpha release under active development, so beware of brittleness, bugs, and changes to the API as the design evolves.
numpy probabilistic-programming bayesian-inference hmc pyro jax inference-algorithmsdatasets - 🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools
🤗Datasets also provides access to +15 evaluation metrics and is designed to let the community easily add and share new datasets and evaluation metrics. 🤗Datasets originated from a fork of the awesome TensorFlow Datasets and the HuggingFace team want to deeply thank the TensorFlow Datasets team for building this amazing library. More details on the differences between 🤗Datasets and tfds can be found in the section Main differences between 🤗Datasets and tfds.
nlp natural-language-processing computer-vision metrics tensorflow numpy evaluation pandas pytorch datasetsxtensor - C++ tensors with broadcasting and lazy computing
Multi-dimensional arrays with broadcasting and lazy computing. xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.
numpy c-plus-plus-14 tensors multidimensional-arraysNumCpp - C++ implementation of the Python Numpy library
This quick start guide is meant as a very brief overview of some of the things that can be done with NumCpp. For a full breakdown of everything available in the NumCpp library please visit the Full Documentation. The main data structure in NumCpp is the NdArray. It is inherently a 2D array class, with 1D arrays being implemented as 1xN arrays. There is also a DataCube class that is provided as a convenience container for storing an array of 2D NdArrays, but it has limited usefulness past a simple container.
c-plus-plus algorithms cpp numpy data-structures scientific-computing mathematical-functions numerical-analysis
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