PyTorch is an open source machine learning framework, used for applications such as computer vision and natural language processing. It is one of the preferred platforms for deep learning research. PyTorch is employed on Python, along with C/C++ libraries, and was designed for overall flexibility. PyTorch is widely used in large companies like Facebook, Twitter, and Google.
PyTorch Tensors: PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers.
Torch is an open source ML library used for creating deep neural. It’s one of the preferred platforms for deep learning research. The framework is built to speed up the process between research prototyping and deployment. The PyTorch framework supports over 200 different mathematical operations. As it simplifies the creation of artificial neural network models. PyTorch is mainly used by data scientists for research and artificial intelligence (AI) applications.
Benefits of PyTorch:
- Offers developers an easy-to-learn, simple-to-code structure that’s based on Python.
- flexibility and speed due to its ease of use.
- Enables easy debugging with popular Python tools.
- Offers scalability and is well-supported on major cloud platforms.
- Offers a user-friendly interface.
- Provides a C++ front-end interface option.
- Includes a rich set of powerful APIs that extend the PyTorch library.
- Deep integration with Python allows popular libraries and packages to quickly write neural network layers in Python.
Deep Learning using PyTorch:
- PyTorch is a Python-based scientific computing package serving two broad purposes:
- A replacement for NumPy to use the power of GPUs and other accelerators.
- An automatic differentiation library that is useful to implement neural networks.