Learn PYTORCH with Real Code Examples

PyTorch provides tools for building neural networks with flexible and dynamic computation graphs.

It is used in research, production, and for AI applications in computer vision, NLP, reinforcement learning, and more.

PyTorch emphasizes ease of use, rapid prototyping, and Python integration, making it popular among researchers.

Tensors and autograd for automatic differentiation

High-level APIs for model building (nn.Module, Sequential)

Optimizers, loss functions, and metrics built-in

Data loading and preprocessing utilities (DataLoader, Dataset)

Support for distributed training and mixed-precision computation

Tensor: n-dimensional array for computations

Module: defines a neural network layer or model

Autograd: automatic differentiation engine

Optimizer: updates model parameters

Dataset/DataLoader: input data management and batching

main.py - main training/testing script

data/ - dataset and preprocessing scripts

models/ - saved PyTorch models

utils/ - helper functions and custom modules

notebooks/ - experiments and prototyping

Define the model using nn.Module

Define loss function and optimizer

Prepare dataset using Dataset and DataLoader

Run training loop using forward pass and backward propagation

Evaluate and test model performance

Optionally export model using TorchScript or ONNX for deployment

Beginner: linear regression or classification with nn.Linear

Intermediate: CNN for image classification

Advanced: RNN, LSTM, GRU, or Transformer models

Expert: custom layers, GANs, or reinforcement learning agents

Enterprise: distributed training or research prototypes in production

PyTorch vs TensorFlow: dynamic vs static graphs, Pythonic vs ecosystem breadth

PyTorch vs Keras: high flexibility vs simplicity

PyTorch vs MXNet: Python-centric vs multi-language

PyTorch vs FastAI: raw library vs high-level wrapper

PyTorch vs JAX: general ML vs numerical/automatic differentiation focus

2016 – PyTorch initial release by Facebook AI Research

2017 – Version 0.2 with expanded features and autograd improvements

2018 – Version 1.0 with stable APIs, TorchScript introduction

2020 – PyTorch 1.5+, improved mobile deployment, JIT optimizations

2025 – Current version with distributed training, extended libraries, and production-ready deployment

Tensor: core data structure

Module: neural network component

Autograd: automatic differentiation

Optimizer: updates parameters

Loss function: guides training