Displaying 1 to 20 from 91 results

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

  •    Python

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.

xarray - N-D labeled arrays and datasets in Python

  •    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.

PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

  •    Jupyter

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.

from-python-to-numpy - An open-access book on numpy vectorization techniques, Nicolas P

  •    HTML

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-100 - 100 numpy exercises (100% complete)

  •    Jupyter

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.

numjs - Like NumPy, in JavaScript

  •    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.

chainer - A flexible framework of neural networks for deep learning

  •    Python

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.

mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

  •    Python

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.

xtensor - C++ tensors with broadcasting and lazy computing

  •    C++

Multi-dimensional arrays with broadcasting and lazy computing. xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.

numpy_exercises - Numpy exercises.

  •    Python

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.

cupy - NumPy-like API accelerated with CUDA

  •    Python

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.

python-cheat-sheet - Python Cheat Sheet NumPy, Matplotlib

  •    Jupyter

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.

tutorials - 机器学习相关教程

  •    Python

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.

100-pandas-puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

  •    Jupyter

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 - Powerful Python Data Analysis Toolkit

  •    Python

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions. It supports aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets, High performance merging and joining of data sets, Time series-functionality, Hierarchical axis indexing and lot more.

Theano - Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

  •    Python

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Its features include tight integration with NumPy, transparent use of a GPU, dynamic C code generation and lot more.

tensorflow-exercises - TensorFlow Exercises - focusing on the comparison with NumPy.

  •    Python

TensorFlow is arugably the most popular deep learning library as of 2017. This is designed to help those who want to familiarize themselves with TensorFlow functions. Particulary, I focus on comparing TensorFlow functions with the equivalent functions in NumPy, the de facto standard numerical computation library. I hope this will help you get comfortable with TensorFlow quickly.