PyTorch Tutorial
Selected Reading
- PyTorch - Discussion
- PyTorch - Useful Resources
- PyTorch - Quick Guide
- PyTorch - Recursive Neural Networks
- PyTorch - Word Embedding
- Sequence Processing with Convents
- PyTorch - Visualization of Convents
- PyTorch - Feature Extraction in Convents
- Training a Convent from Scratch
- PyTorch - Introduction to Convents
- PyTorch - Datasets
- PyTorch - Recurrent Neural Network
- PyTorch - Convolutional Neural Network
- PyTorch - Linear Regression
- PyTorch - Loading Data
- PyTorch - Terminologies
- Neural Networks to Functional Blocks
- Implementing First Neural Network
- Machine Learning vs. Deep Learning
- Universal Workflow of Machine Learning
- PyTorch - Neural Network Basics
- Mathematical Building Blocks of Neural Networks
- PyTorch - Installation
- PyTorch - Introduction
- PyTorch - Home
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- Computer Glossary
- HR Interview Questions
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- Questions and Answers
- UPSC IAS Exams Notes
PyTorch - Introduction
PyTorch - Introduction
PyTorch is defined as an open source machine learning pbrary for Python. It is used for apppcations such as natural language processing. It is initially developed by Facebook artificial-intelpgence research group, and Uber’s Pyro software for probabipstic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C pbraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibipty features that made Lua-based Torch.
Features
The major features of PyTorch are mentioned below −
Easy Interface − PyTorch offers easy to use API; hence it is considered to be very simple to operate and runs on Python. The code execution in this framework is quite easy.
Python usage − This pbrary is considered to be Pythonic which smoothly integrates with the Python data science stack. Thus, it can leverage all the services and functionapties offered by the Python environment.
Computational graphs − PyTorch provides an excellent platform which offers dynamic computational graphs. Thus a user can change them during runtime. This is highly useful when a developer has no idea of how much memory is required for creating a neural network model.
PyTorch is known for having three levels of abstraction as given below −
Tensor − Imperative n-dimensional array which runs on GPU.
Variable − Node in computational graph. This stores data and gradient.
Module − Neural network layer which will store state or learnable weights.
Advantages of PyTorch
The following are the advantages of PyTorch −
It is easy to debug and understand the code.
It includes many layers as Torch.
It includes lot of loss functions.
It can be considered as NumPy extension to GPUs.
It allows building networks whose structure is dependent on computation itself.
TensorFlow vs. PyTorch
We shall look into the major differences between TensorFlow and PyTorch below −
| PyTorch | TensorFlow |
|---|---|
PyTorch is closely related to the lua-based Torch framework which is actively used in Facebook. |
TensorFlow is developed by Google Brain and actively used at Google. |
PyTorch is relatively new compared to other competitive technologies. |
TensorFlow is not new and is considered as a to-go tool by many researchers and industry professionals. |
PyTorch includes everything in imperative and dynamic manner. |
TensorFlow includes static and dynamic graphs as a combination. |
Computation graph in PyTorch is defined during runtime. |
TensorFlow do not include any run time option. |
PyTorch includes deployment featured for mobile and embedded frameworks. |
TensorFlow works better for embedded frameworks. |