Simple Linear Regression - Pytorch Typing CST Test

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Simple Linear Regression — Pytorch Code

A minimal PyTorch example performing linear regression on sample data.

import torch
import torch.nn as nn

# Sample data
x_train = torch.tensor([[1.0],[2.0],[3.0],[4.0]])
y_train = torch.tensor([[2.0],[4.0],[6.0],[8.0]])

# Define model
model = nn.Linear(1,1)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Train the model
for epoch in range(500):
	optimizer.zero_grad()
	outputs = model(x_train)
	loss = criterion(outputs, y_train)
	loss.backward()
	optimizer.step()

# Predict
with torch.no_grad():
	y_pred = model(torch.tensor([[10.0]]))
print("Prediction for 10:", y_pred.item())

Pytorch Language Guide

PyTorch is an open-source machine learning library developed by Facebook’s AI Research (FAIR). It is widely used for deep learning research, model prototyping, and production deployment, offering dynamic computation graphs and a Pythonic interface.

Primary Use Cases

▸Deep learning for computer vision tasks (CNNs, object detection, segmentation)
▸Natural language processing (RNNs, Transformers, BERT, GPT)
▸Reinforcement learning and robotics
▸Time series forecasting and generative modeling
▸Rapid prototyping of custom neural networks for research or production

Notable Features

▸Dynamic computation graphs (eager execution by default)
▸Native Python support and integration
▸Strong GPU acceleration via CUDA
▸TorchScript for model deployment
▸Extensive ecosystem: TorchVision, TorchText, TorchAudio, PyTorch Lightning

Origin & Creator

PyTorch was developed by Facebook’s AI Research (FAIR) team and released in 2016 as a flexible alternative to TensorFlow and other ML frameworks.

Industrial Note

PyTorch is highly favored in research communities for experimenting with novel architectures and techniques due to its dynamic computation graph and easy debugging.

More Pytorch Typing Exercises

PyTorch Simple Neural Network PyTorch Logistic Regression PyTorch Convolutional Network Example PyTorch RNN Example PyTorch Optimizer Example PyTorch Custom Loss Example PyTorch GPU Tensor Example PyTorch Dataset and DataLoader Example PyTorch Transfer Learning Example

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Simple Linear Regression — Pytorch Code

A minimal PyTorch example performing linear regression on sample data.

import torch import torch.nn as nn # Sample data x_train = torch.tensor([[1.0],[2.0],[3.0],[4.0]]) y_train = torch.tensor([[2.0],[4.0],[6.0],[8.0]]) # Define model model = nn.Linear(1,1) criterion = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # Train the model for epoch in range(500): optimizer.zero_grad() outputs = model(x_train) loss = criterion(outputs, y_train) loss.backward() optimizer.step() # Predict with torch.no_grad(): y_pred = model(torch.tensor([[10.0]])) print("Prediction for 10:", y_pred.item())

Pytorch Language Guide

Primary Use Cases

▸Deep learning for computer vision tasks (CNNs, object detection, segmentation)
▸Natural language processing (RNNs, Transformers, BERT, GPT)
▸Reinforcement learning and robotics
▸Time series forecasting and generative modeling
▸Rapid prototyping of custom neural networks for research or production

Notable Features

▸Dynamic computation graphs (eager execution by default)
▸Native Python support and integration
▸Strong GPU acceleration via CUDA
▸TorchScript for model deployment
▸Extensive ecosystem: TorchVision, TorchText, TorchAudio, PyTorch Lightning

Origin & Creator

PyTorch was developed by Facebook’s AI Research (FAIR) team and released in 2016 as a flexible alternative to TensorFlow and other ML frameworks.

Industrial Note

PyTorch is highly favored in research communities for experimenting with novel architectures and techniques due to its dynamic computation graph and easy debugging.