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✨ Restructure README: Lead with big picture and key differentiators
- Move 'The Big Picture: Why Build from Scratch?' to the top - Add prominent 'What Makes TinyTorch Different' section highlighting unique value - Emphasize build-first philosophy vs traditional 'use' frameworks approach - Show concrete code comparison: traditional vs TinyTorch approach - Better highlight real production skills, progressive mastery, instant feedback - Reorganize content flow: vision → differentiators → practical details
This commit is contained in:
Vijay Janapa Reddi
155
README.md
155
README.md
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[](https://python.org)
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[](https://mlsysbook.github.io/TinyTorch/)
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A hands-on [Machine Learning Systems](https://mlsysbook.ai) course companion where students don’t just learn ML — they build it.
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Tiny🔥Torch is the minimalist, code-first companion to any machine learning systems course. It embraces a “start small, go deep” philosophy—starting with tensors and layers, and guiding learners through each system component, all the way to a complete MLOps pipelines built from scratch in their own codebase—albeit within a deliberately small-scale educational framework.
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📚 **[Read the Interactive Course →](https://mlsysbook.github.io/TinyTorch/)**
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---
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## 🏗️ **The Big Picture: Why Build from Scratch?**
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**Most ML education teaches you to _use_ frameworks.** TinyTorch teaches you to _understand_ them.
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```python
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Traditional ML Course: TinyTorch Approach:
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├── import torch ├── class Tensor:
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├── model = nn.Linear(10, 1) │ def __add__(self, other): ...
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├── loss = nn.MSELoss() │ def backward(self): ...
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└── optimizer.step() ├── class Linear:
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│ def forward(self, x):
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│ return x @ self.weight + self.bias
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├── def mse_loss(pred, target):
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│ return ((pred - target) ** 2).mean()
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├── class SGD:
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│ def step(self):
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└── param.data -= lr * param.grad
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Go from "How does this work?" 🤷 to "I implemented every line!" 💪
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```
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**Result:** You become the person others come to when they need to understand "how PyTorch actually works under the hood."
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---
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## 🌟 **What Makes TinyTorch Different**
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### **🔬 Build-First Philosophy**
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- **No black boxes**: Implement every component from scratch
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- **Immediate ownership**: Use YOUR code in real neural networks
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- **Deep understanding**: Know exactly how each piece works
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### **🚀 Real Production Skills**
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- **Professional workflow**: Development with `tito` CLI, automated testing
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- **Real datasets**: Train on CIFAR-10, not toy data
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- **Production patterns**: MLOps, monitoring, optimization from day one
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### **🎯 Progressive Mastery**
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- **Start simple**: Implement `hello_world()` function
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- **Build systematically**: Each module enables the next
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- **End powerful**: Deploy production ML systems with monitoring
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### **⚡ Instant Feedback**
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- **Code works immediately**: No waiting to see results
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- **Visual progress**: Success indicators and system integration
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- **"Aha moments"**: Watch your `ReLU` power real neural networks
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---
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## 🎯 What You'll Build
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* **Complete ML Framework** — Your own PyTorch-style toolkit, from tensors to MLOps
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@@ -79,6 +124,57 @@ tito nbdev build # Update package
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---
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## 🧠 Pedagogical Framework: Build → Use → Reflect
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### **Example: How You'll Master Activation Functions**
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**🔧 Build:** Implement ReLU from scratch
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```python
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def relu(x):
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# YOU implement this function
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return ??? # What should this be?
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```
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**🚀 Use:** Immediately use your own code
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```python
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from tinytorch.core.activations import ReLU # YOUR implementation!
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layer = ReLU()
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output = layer.forward(input_tensor) # Your code working!
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```
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**💡 Reflect:** See it working in real networks
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```python
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# Your ReLU is now part of a real neural network
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model = Sequential([
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Dense(784, 128),
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ReLU(), # <-- Your implementation
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Dense(128, 10)
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])
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```
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**This pattern repeats for every component** — you build it, use it immediately, then see how it fits into larger systems.
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---
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## 🎓 Teaching Philosophy
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### **No Black Boxes**
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* Build every component from scratch
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* Understand performance trade-offs
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* See how engineering decisions impact ML outcomes
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### **Production-Ready Thinking**
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* Use real datasets (CIFAR-10, MNIST)
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* Implement proper testing and benchmarking
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* Learn MLOps and system design principles
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### **Iterative Mastery**
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* Each module builds on previous work
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* Immediate feedback through inline testing
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* Progressive complexity with solid foundations
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---
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## 📖 Documentation
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### **Interactive Jupyter Book**
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@@ -126,57 +222,6 @@ git push origin main # Triggers documentation deployment
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---
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## 🧠 Pedagogical Framework: Build → Use → Reflect
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### **Real Engineering, Real Understanding**
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1. **Build** — Implement `ReLU()` activation function
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2. **Use** — Apply it via `tinytorch.core.activations.ReLU()`
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3. **Reflect** — Understand its role in neural network design
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4. **Iterate** — Extend knowledge with each module
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### **Example Learning Cycle**
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```python
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# Step 1: You implement this in tensor_dev.py
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class Tensor:
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def __init__(self, data):
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self.data = np.array(data)
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def __add__(self, other):
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return Tensor(self.data + other.data)
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# Step 2: Export and use in your framework
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from tinytorch.core.tensor import Tensor
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a = Tensor([1, 2, 3])
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b = Tensor([4, 5, 6])
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result = a + b # Your implementation at work!
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# Step 3: Apply to real problems
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model = Sequential([Dense(784, 128), ReLU(), Dense(128, 10)])
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```
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---
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## 🎓 Teaching Philosophy
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### **No Black Boxes**
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* Build every component from scratch
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* Understand performance trade-offs
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* See how engineering decisions impact ML outcomes
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### **Production-Ready Thinking**
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* Use real datasets (CIFAR-10, MNIST)
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* Implement proper testing and benchmarking
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* Learn MLOps and system design principles
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### **Iterative Mastery**
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* Each module builds on previous work
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* Immediate feedback through inline testing
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* Progressive complexity with solid foundations
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---
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## 📁 Project Structure
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```
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