Why use PyTorch?
- It supports tensor computation: Tensor is the data structure that is similar to the networks, array. It is an n-dimensional array that contains the data. We can perform arbitrary numeric computation on these arrays using the APIs.
- It provides Dynamic Graph Computation: This feature allows us to define the computational graphs dynamically during runtime. This makes it more flexible than the static computation graphs approach in which where the graph structure is fixed and defined before execution,
- It provides the Automatic Differentiation: The Autograd package automatically computes the gradients that are crucial for training the model using optimization algorithms. Thus, we can perform operations on tensors without manually calculating gradients.
- It has Support for Python: It has native support for the Python programming language. Thus, we can easily integrate with existing Python workflows and libraries. This is the reason why it is used by the machine learning and data science communities.
- It has its production environment: PyTorch has the TorchScript which is the high-performance environment for serializing and executing PyTorch models. You can easily compile PyTorch models into a portable intermediate representation (IR) format. Due to this, we can deploy the model on various platforms and devices without requiring the original Python code.
Start learning PyTorch for Beginners
Machine Learning helps us to extract meaningful insights from the data. But now, it is capable of mimicking the human brain. This is done using neural networks, which contain the various interconnected layers of nodes containing the data. This data is passed to forward layers. Subsequently, the model learns from the data and predicts output for the new data.
PyTorch helps us to create and train these neural networks that act like our brains and learn from the data.
Table of Content
- What is Pytorch?
- Why use PyTorch?
- How to install Pytorch ?
- PyTorch Basics
- Autograd: Automatic Differentiation in PyTorch
- Neural Networks in PyTorch
- Working with Data in PyTorch
- Intermediate Topics in PyTorch
- Validation and Testing
- Frequently Asked Questions
Contact Us