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About PyTorch

PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab, known for its dynamic computational graphs and ease of use with Python. It supports various applications such as computer vision, natural language processing, and reinforcement learning, and features tools like TorchScript for production optimization. Compared to TensorFlow, PyTorch offers a more intuitive syntax and a strong research community, while TensorFlow is more focused on production deployment.

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26 views2 pages

About PyTorch

PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab, known for its dynamic computational graphs and ease of use with Python. It supports various applications such as computer vision, natural language processing, and reinforcement learning, and features tools like TorchScript for production optimization. Compared to TensorFlow, PyTorch offers a more intuitive syntax and a strong research community, while TensorFlow is more focused on production deployment.

Uploaded by

shiva gowtham kale
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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About PyTorch

PyTorch is an open-source deep learning framework developed by Facebook’s AI Research


lab (FAIR). It is widely used for developing machine learning and deep learning models,
especially in research and production.

🧠 Key Features

 Dynamic Computational Graphs (Define-by-Run): PyTorch builds the computational


graph on-the-fly, making it more intuitive and flexible for debugging and
experimentation.

 Pythonic and Easy to Use: Designed to integrate seamlessly with Python, it feels
natural for Python developers and data scientists.

 Tensor Computation: Similar to NumPy but with GPU acceleration.

 Autograd Module: Automatically computes gradients, making backpropagation


effortless.

 TorchScript: Allows models to be converted into a production-optimized form while


still using the PyTorch interface.

 Community and Ecosystem: A vibrant ecosystem including tools like TorchVision,


TorchText, and PyTorch Lightning, and integration with ONNX for model export.

⚙️Common Use Cases

 Computer Vision (CV)

 Natural Language Processing (NLP)

 Reinforcement Learning (RL)

 Generative models (GANs, VAEs)

 Scientific computing

🚀 PyTorch vs TensorFlow

Feature PyTorch TensorFlow

Graphs Dynamic Static (eager mode available)

Syntax Pythonic Verbose

Community Strong in research Strong in production

Deployment TorchScript, ONNX TensorFlow Serving, TFLite

🛠 Example Code
python

CopyEdit

import torch

import torch.nn as nn

import torch.optim as optim

# A simple linear model

model = nn.Linear(10, 1)

criterion = nn.MSELoss()

optimizer = optim.SGD(model.parameters(), lr=0.01)

# Dummy input and target

x = torch.randn(5, 10)

y = torch.randn(5, 1)

# Training step

optimizer.zero_grad()

output = model(x)

loss = criterion(output, y)

loss.backward()

optimizer.step()

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