NumPy
NumPy是用Python进行科学计算的基本软件包。 它包含以下内容:
一个强大的N维数组对象
复杂的(广播)功能
用于集成C / C ++和Fortran代码的工具
有用的线性代数,傅里叶变换和随机数能力
除了明显的科学用途外,NumPy还可以用作通用数据的高效多维容器。 任意的数据类型可以被定义。 这使得NumPy能够与各种各样的数据库无缝,快速地整合。
NumPy是根据BSD许可证进行许可的,只需很少的限制即可重复使用。
Getting Started
- Getting NumPy
- Installing the SciPy Stack
- NumPy and SciPy documentation page
- NumPy Tutorial
- NumPy for MATLAB© Users
- NumPy functions by category
- NumPy Mailing List
For more information on the SciPy Stack (for which NumPy provides the fundamental array data structure), see scipy.org.
Pytorch
PyTorch是一个python包,提供了两个高级功能:张量计算(如numpy)与强大的GPU加速深度神经网络建立在一个基于磁带的autograd系统上你可以重用你最喜欢的python软件包,比如numpy,scipy和Cython,以便在需要时扩展PyTorch。在粒度级别上,PyTorch是一个由以下组件组成的库:
Package | Description |
---|---|
torch | a Tensor library like NumPy, with strong GPU support |
torch.autograd | a tape based automatic differentiation library that supports all differentiable Tensor operations in torch |
torch.nn | a neural networks library deeply integrated with autograd designed for maximum flexibility |
torch.optim | an optimization package to be used with torch.nn with standard optimization methods such as SGD, RMSProp, LBFGS, Adam etc. |
torch.multiprocessing | python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and hogwild training. |
torch.utils | DataLoader, Trainer and other utility functions for convenience |
torch.legacy(.nn/.optim) | legacy code that has been ported over from torch for backward compatibility reasons |
numpy替代使用GPU的力量;
一个深度学习研究平台,提供最大的灵活性和速度。