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PyTorch Beginner Guide

Sep 26, 2025

Overview

This lecture is a beginner-friendly introduction to PyTorch, covering its core features, basic workflow, and how to build and train a simple neural network using Kaggle notebooks with GPU acceleration.

Introduction to PyTorch and Setup

  • PyTorch is a widely used deep learning framework developed by Meta (Facebook) and used in major AI models like GPT-3, GPT-4, and Tesla Autopilot.
  • TensorFlow, created by Google, is another major deep learning framework.
  • Kaggle notebooks are similar to Jupyter or Colab, supporting free GPU use and providing datasets, models, and competitions.
  • PyTorch can be installed via pip, supporting both CPU and GPU (CUDA) configurations.

Tensors and Basic Operations

  • Tensors in PyTorch are multi-dimensional arrays, similar to NumPy arrays but optimized for GPU use.
  • Tensors can be created, sliced, and operated on with familiar syntax.
  • Basic operations include element-wise addition, multiplication, and matrix multiplication.

Automatic Differentiation

  • PyTorch enables automatic differentiation for tensors, allowing for computation of gradients.
  • Setting requires_grad=True tracks tensor operations for gradient computation during backpropagation.

Working with Datasets and DataLoaders

  • Custom datasets inherit from torch.utils.data.Dataset and define data, length, and item access methods.
  • DataLoaders efficiently handle batching, shuffling, and loading of data, especially for large datasets.

Building Neural Networks

  • Neural networks in PyTorch are defined as classes inheriting from nn.Module.
  • Layers are defined in the constructor (__init__), and the forward pass is implemented in the forward method.
  • Fully connected (linear) layers and activation functions (e.g., ReLU) are commonly used.

Training Loop: Loss Function and Optimizer

  • Loss functions (e.g., Mean Squared Error) measure prediction error and must be differentiable.
  • Optimizers (e.g., SGD) update model parameters based on gradients to minimize loss.
  • The training loop involves forward pass, loss computation, backward pass (gradient computation), and optimizer step.

GPU Acceleration

  • GPU can be activated and detected in Kaggle by switching the runtime and checking with torch.cuda.is_available().
  • Tensors and models can be moved to GPU with .to(device).
  • Training on GPU offers significant speedup for large models and datasets.

Saving and Loading Models

  • Model weights can be saved with torch.save(model.state_dict(), filename).
  • Models are loaded by re-instantiating the architecture and loading weights with model.load_state_dict(torch.load(filename)).
  • Loaded models can be used for inference on new data without retraining.

Key Terms & Definitions

  • Tensor — Multidimensional array used as basic data structure in PyTorch.
  • Dataset — Class defining data samples and their retrieval logic.
  • DataLoader — Utility for batching and shuffling data for model training.
  • Autograd — PyTorch system for automatic differentiation.
  • Module — Base class for all neural network layers and models.
  • Loss Function — Function measuring the error between predictions and true values.
  • Optimizer — Algorithm updating model parameters to minimize loss.
  • CUDA — NVIDIA’s parallel computing platform for GPU acceleration.

Action Items / Next Steps

  • Explore the linked public Kaggle notebook for code examples.
  • Practice by creating simple models, experimenting with datasets, and running on GPU.
  • Read or watch further tutorials on PyTorch for advanced topics like CNNs or larger projects.

Full transcript