PyTorch has 232,000 stars on GitHub.

TinyTorch teaches you to build PyTorch from scratch.

Don't import it. Build it.

The curriculum that turns ML users into ML engineers (save this):

Foundation Tier (Modules 1-7):

→ Build tensors from NumPy arrays

→ Implement backpropagation by hand

→ Code gradient descent from first principles

Skip this → You'll debug NaN losses forever

Architecture Tier (Modules 8-13):

→ Build CNNs that actually work

→ Implement attention mechanisms

→ Create transformers from scratch

Skip this → You're stuck with pre-built architectures

Optimization Tier (Modules 14-19):

→ Profile memory bottlenecks

→ Quantize models (8-16× smaller)

→ Accelerate inference (12-40× faster)

Skip this → Your models die at scale

Capstone: Torch Olympics

→ Build complete ML system

→ Compete on speed, accuracy, size

→ Prove you understand the stack

Historical milestones you'll recreate:

1957: Perceptron

1969: XOR Crisis solution

1986: Backpropagation

1998: CNN Revolution

2017: Transformer Era

2018: MLPerf benchmarks

Traditional ML education:

```

import torch

model = torch.nn.Linear(784, 10)

# When this breaks, you're stuck

```

TinyTorch approach:

```

from tinytorch import Linear # YOUR code

model = Linear(784, 10) # YOU built this

# You know exactly why it works

```

Most courses teach you to import torch.

This one teaches you to build it.

Harvard's Prof. Vijay Janapa Reddi.

🔥 Start here: mlsysbook.ai/tinytorch

Time: 20 modules, 2 hours each.

You can't debug what you don't understand.

You can't optimize what you didn't build.

💾 Save this. It's how you go from using ML to engineering ML.

♻️ Repost if you're tired of black-box frameworks.