Intro to PyTorch Library | Python

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PyTorch Library in Python

PyTorch is software, purposely a machine learning library for the programming language Python, used for applications and based on the Torch library, such as deep learning, natural language processing.

KEY FEATURES and CAPABILITIES

–  Hybrid Front-End
–  Distributed Training
–  Python-First
–  Tools and Libraries
–  NATIVE ONNX SUPPORT
–  C++ FRONT-END
–  CLOUD PARTNERS

“For building neural networks, Py-Torch has a unique way of: using and replaying a tape recorder. PyTorch are it’s multi GPU support, custom data loaders and simplified pre-processors.

– Most of the frameworks such as TensorFlow, Theano, Caffe and CNTK have a stationary view of the world. One has to build a neural network, and then reuse the same structure again and again. Changing the behaviour of the network implies starting from scratch.

– Reverse-mode auto-differentiation technique we use with that, which allows you to modify the way your network behaves arbitrarily with zero lag or overhead. it’s one of the fastest implementations of it to date While this technique is not unique to this library,. You get the speedy and flexibility for your research.

The main elements we should get to know when starting out with PyTorch are:

1. PyTorch Tensors
2. Mathematical Operations
3. Autograd module
4. Optim module and
5. nn module

Why would we use PyTorch to construct models of deep learning ?

a. Easy to use API
b. Python support
c. Dynamic computation graphs

Pytorch VS Tesorflow

While Tensorflow and PyTorch are both open-source, two distinct wizards have developed them. Tensorflow is based on Theano and has been created by Google, while PyTorch has been created by Facebook and is based on Torch.

– The most significant distinction between the two is how computational graphs are defined by these frameworks. It thinks in a vibrant graph while Tensorflow produces a static graph. What does that imply, then? You must first describe the entire model’s computation graph in Tensorflow and then run your ML model. But you can define / manipulate your on – the-go graph in Py Torch. This is especially useful when using inputs of variable duration in RNNs.

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