Recreate ML History

Build Your Own ML Framework

🚧 Preview · Classroom ready 2026

Hands-on labs for the Machine Learning Systems textbook

Don't import it. Build it.

From tensors to systems. An educational framework for building and optimizing ML—understand how PyTorch, TensorFlow, and JAX really work.

TinyTorch: Build AI Like Bricks

🔧 Build each piece — Tensors, autograd, attention. No magic imports.
📚 Recreate history — Perceptron → CNN → Transformers → MLPerf.
⚡ Understand systems — Memory, compute, optimization trade-offs.
🎯 Debug anything — OOM, NaN, slow training—because you built it.

Recreate ML History#

Walk through ML history by rebuilding its greatest breakthroughs with YOUR TinyTorch implementations. Click each milestone to see what you’ll build and how it shaped modern AI.

1957
The Perceptron
The first trainable neural network
Input → Linear → Sigmoid → Output
1969
XOR Crisis
Minsky & Papert expose limits of single-layer networks
Input → Linear → Sigmoid → FAIL!
1986
MLP Revival
Backpropagation enables deep learning (95%+ MNIST)
Images → Flatten → Linear → ... → Classes
1998
CNN Revolution 🎯
Spatial intelligence unlocks computer vision (75%+ CIFAR-10)
Images → Conv → Pool → ... → Classes
2017
Transformer Era
Attention launches the LLM revolution
Tokens → Attention → FFN → Output
2018–Present
MLPerf Benchmarks
Production optimization (8-16Ă— smaller, 12-40Ă— faster)
Profile → Compress → Accelerate

Why Build Instead of Use?#

Traditional ML Education

import torch
model = torch.nn.Linear(784, 10)
output = model(input)
# When this breaks, you're stuck

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

TinyTorch: Build → Use → Reflect

# BUILD it yourself
class Linear:
    def forward(self, x):
        return x @ self.weight + self.bias

# USE it on real data
loss.backward()  # YOUR autograd

Advantage: You can debug it because you built it.

Learning Path#

Four progressive tiers take you from foundations to production systems:

The Big Picture • Getting Started • Preface

Is This For You?#

🎓 Students

Taking ML courses, want to understand what's behind import torch

👩‍🏫 Instructors

Teaching ML systems with ready-made hands-on labs

🚀 Self-learners

Career changers or hobbyists going deeper than tutorials

Prerequisites: Python + basic linear algebra. No ML experience required.

Join the Community#

See learners building ML systems worldwide

Add yourself to the map • Share your progress • Connect with builders

Next Steps: Quick Start (15 min) • The Big Picture • Community

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