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Remove FAQ section from website intro

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- Keep intro focused and clean
- Let the content speak for itself
- Avoid over-explaining before people even start
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Vijay Janapa Reddi
2025-07-16 12:15:33 -04:00
parent 1b4c892b14
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@@ -198,57 +198,6 @@ Want to see what TinyTorch feels like? **[Launch the Setup chapter](chapters/01-
---
## ❓ **Common Questions**
<details>
<summary><strong>🧮 "Do I need to know advanced math to succeed?"</strong></summary>
**You need high school math + willingness to learn.** We explain the math as we go:
- **Linear algebra**: We explain matrix multiplication when we build Dense layers
- **Calculus**: We cover derivatives when implementing backpropagation
- **Statistics**: We introduce concepts like gradients in context of optimization
**Key insight:** You learn the math by implementing it, not the other way around. Most students find this more intuitive than traditional math courses.
</details>
<details>
<summary><strong>✅ "How do I know if I'm implementing things correctly?"</strong></summary>
**Built-in validation at every step:**
- **200+ automated tests** that check your implementations
- **Progressive testing**: Start simple, then add complexity
- **Clear error messages**: "Your ReLU should return 0 for negative inputs"
- **Expected outputs**: Know exactly what your code should produce
**Example:** Your tensor multiplication either passes the test or gets specific feedback about what went wrong.
</details>
<details>
<summary><strong>🔗 "Can I skip around or must I do modules in order?"</strong></summary>
**Designed for flexibility with recommended paths:**
- **Core foundation**: Modules 1-4 build on each other (do in order)
- **Parallel tracks**: Modules 5-8 can be done in different sequences
- **Advanced modules**: Pick what interests you most
**Smart approach:** Do Setup → Tensor → Activations in order, then choose your adventure.
</details>
<details>
<summary><strong>🚀 "Will this actually work with real data and real problems?"</strong></summary>
**Yes - we use production datasets from day one:**
- **CIFAR-10**: Train on 60,000 real images, not toy data
- **Standard benchmarks**: Compare your results to published papers
- **Real performance**: Your CNN will achieve 85%+ accuracy on image classification
- **Production patterns**: CLI tools, testing, packaging like professional frameworks
**This isn't a toy - it's a real framework that handles real problems.**
</details>
---
## 🙏 **Acknowledgments**
TinyTorch originated from CS249r: Tiny Machine Learning Systems at Harvard University. We're inspired by projects like [tinygrad](https://github.com/geohot/tinygrad), [micrograd](https://github.com/karpathy/micrograd), and [MiniTorch](https://minitorch.github.io/) that demonstrate the power of minimal implementations.