Don't import torch. Build it.

Don’t import torch. Build it.#

Build Your Own ML Framework

🚧 Preview · Classroom ready 2026

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.

🔧 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.

1958

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 (8-16× smaller, 12-40× faster)

Profile → Compress → Accelerate

Why Build Instead of Use?#

"Building systems creates irreversible understanding."

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.

Contents