The ML Engineer's Path #1intermediate

PyTorch from Zero to Production

The framework that powers Tesla Autopilot, Meta AI, and most ML research. Start with tensors, build CNNs and Transformers from scratch, then deploy your model as a production API. Every lesson has a hands-on notebook.

What You Build

An end-to-end ML-powered application: custom model trained in PyTorch, served via TorchServe, with a working frontend

Sections

Lessons

Estimated

Concepts

Prerequisites

Zero to AI-Powered Developer

pytorchmldeep-learningcnntransformersdeployment

Dev Jordan

walkthrough · casual

First 4 lessons are free (29% of the course). No login required.

Why PyTorch?

Why PyTorch dominates research and production, and what tensors actually are.

Why PyTorch Won

20 min Build in Public

Tweet: Tweet: 'Starting my ML journey with PyTorch. The framework researchers and industry actually use.'

Tensors Are Just Arrays (With Superpowers)

25 min Ship

Ship: Create tensors, reshape them, run operations on GPU

Build Your First Models

Neural networks, training loops, data loading — the foundations.

Your First Neural Network in 20 Lines

35 min Ship

Ship: A working classifier that predicts handwritten digits (MNIST)

The Training Loop Explained

30 min Ship

Ship: Training loop with loss curve plotted

Preview — content blurs halfway through

Datasets, DataLoaders & Transforms

30 min

Loss Functions & Optimizers: Picking the Right Ones

25 min

Unlock the rest of this course

$5 for full course or $25/mo for everything

See pricing

Architectures That Matter

CNNs, RNNs, Transformers — the architectures powering real products.

CNNs: See Like a Machine

40 min

Transfer Learning: Stand on Giants' Shoulders

35 min

RNNs & LSTMs: Sequence Data

35 min

Transformers from Scratch in PyTorch

45 min

Production & Deployment

Serving models, exporting to ONNX, deploying at the edge.

Saving, Loading & Checkpointing Models

20 min

TorchServe: Deploy Your Model as an API

35 min

ONNX Export & Edge Deployment

30 min

Final Ship

Build and deploy a complete ML-powered application.

Ship an ML-Powered App

50 min

Next Course

TensorFlow, JAX & the Google AI Stack

Google builds more ML infrastructure than anyone. Learn TensorFlow + Keras for production, JAX for research-speed training, TPUs for scale, and Vertex AI for deployment. Plus the Gemini API for multi-modal AI.

PyTorch from Zero to Production

What You Build

An end-to-end ML-powered application: custom model trained in PyTorch, served via TorchServe, with a working frontend