PyTorch 101

Welcome to my PyTorch 101 series! This comprehensive guide covers everything from basic tensors to production deployment.

My PyTorch Journey

I remember staring at NumPy arrays, manually calculating gradients for a simple neural network. Three days of debugging backward pass bugs. Then I discovered PyTorch's autograd - automatic differentiation that just works.

That moment changed how I build ML systems.

This series shares what I learned building production deep learning systems with PyTorch.

Series Overview

Part 1: Introduction to PyTorch and Tensors

• What is PyTorch and why it matters

• Installation and setup

• Tensor fundamentals and operations

• GPU acceleration basics

• Real example: Migrating from NumPy to PyTorch for performance

Part 2: Autograd and Automatic Differentiation

• Understanding automatic differentiation

• Computational graphs and gradients

• Backpropagation mechanics

• Custom gradient functions

• Real example: Building a custom loss function with autograd

Part 3: Building Neural Networks with torch.nn

• nn.Module fundamentals

• Layers and activations

• Building custom architectures

• Loss functions and metrics

• Real example: Image classifier from scratch

Part 4: Training and Optimization

• Training loop patterns

• Optimizers (SGD, Adam, AdamW)

• Data loaders and datasets

• Learning rate scheduling

• Real example: Complete training pipeline for production

Part 5: Production Deployment and Optimization

• Model saving and loading

• TorchScript and ONNX export

• Quantization and optimization

• Serving models in production

• Real example: Deploying models with FastAPI and Docker

Who This Series Is For

You'll benefit from this series if you:

• Have Python programming experience

• Want to learn deep learning frameworks

• Need to deploy ML models in production

• Are transitioning from other frameworks (TensorFlow, JAX)

Prerequisites:

• Python 3.8+

• Basic understanding of neural networks (helpful but not required)

• Familiarity with NumPy (helpful but not required)

What Makes This Series Different

Personal experience: Every example comes from real projects I've built.

No fake scenarios: Real production challenges and solutions.

Python 3 focus: Modern Python with type hints and best practices.

Complete examples: Full working code, not just snippets.

Production-ready: Patterns you can use in real systems.

Tools We'll Use

• PyTorch 2.0+: Core deep learning framework

• torchvision: Computer vision utilities

• CUDA: GPU acceleration (optional)

• tensorboard: Training visualization

• ONNX: Model export

• Python 3.10+: Modern Python features

Learning Path

Follow in order for best results:

1. Start with Part 1 if new to PyTorch

2. Part 2 for understanding autograd (crucial!)

3. Part 3 for building models

4. Part 4 for training

5. Part 5 for production deployment

Already know PyTorch basics? Jump to Part 4 or Part 5.

My Background

I've been building production ML systems for several years. Started with scikit-learn, moved to TensorFlow, then discovered PyTorch and never looked back.

PyTorch clicked for me because:

• Pythonic API - feels natural

• Dynamic computation graphs - debug like normal Python

• Strong community and ecosystem

• Production-ready with TorchScript and ONNX

What You'll Build

By the end of this series, you'll build:

• ✓ Image classification system

• ✓ Custom neural network architectures

• ✓ Complete training pipelines

• ✓ Production-ready model serving API

• ✓ Optimized models for deployment

Get Started

Ready to dive in? Start with Part 1: Introduction to PyTorch and Tensors.

Questions or feedback? Each article has examples you can run yourself.

---

This series focuses on practical, production-ready PyTorch patterns based on real-world experience. No theoretical fluff - just code that works.

Let's build something amazing with PyTorch! 🔥