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136 lines
4.5 KiB
Markdown
136 lines
4.5 KiB
Markdown
# Prerequisites & Self-Assessment
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**Purpose**: Ensure you have the foundational knowledge to succeed in TinyTorch and discover complementary resources for deeper learning.
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---
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## Core Requirements
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You need TWO things to start building:
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### 1. Python Programming
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- Comfortable writing functions and classes
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- Familiarity with basic NumPy arrays
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- No ML framework experience required—you'll build your own!
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**Self-check**: Can you write a Python class with `__init__` and methods?
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### 2. Basic Linear Algebra
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- Understand matrix multiplication conceptually
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- Know what a gradient (derivative) represents at a high level
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**Self-check**: Do you know what multiplying two matrices means?
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**That's it. You're ready to start building.**
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---
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## "Nice to Have" Background
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**We teach these concepts as you build**—you don't need them upfront:
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- **Calculus (derivatives)**: Module 05 (Autograd) teaches this through implementation
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- **Deep learning theory**: You'll learn by building, not lectures
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- **Advanced NumPy**: We introduce operations as needed in each module
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**Learning Philosophy**: TinyTorch teaches ML systems through implementation. You'll understand backpropagation by building it, not by watching lectures about it.
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---
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## Self-Assessment: Which Learning Path Fits You?
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### Path A: Foundation-First Builder (Recommended for most)
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**You are:**
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- Strong Python programmer
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- Curious about ML systems
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- Want to understand how frameworks work
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**Start with**: Module 01 (Tensor)
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**Best for**: CS students, software engineers transitioning to ML, anyone wanting deep systems understanding
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### Path B: Focused Systems Engineer
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**You are:**
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- Professional ML engineer
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- Need specific optimization skills
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- Want production deployment knowledge
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**Start with**: Review Foundation Tier (01-07), focus on Optimization Tier (14-19)
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**Best for**: Working engineers debugging production systems, performance optimization specialists
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### Path C: Academic Researcher
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**You are:**
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- ML theory background
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- Need implementation skills
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- Want to prototype novel architectures
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**Start with**: Module 01, accelerate through familiar concepts
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**Best for**: PhD students, research engineers, anyone implementing custom operations
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---
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## Complementary Learning Resources
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### Essential Systems Context
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**[Machine Learning Systems](https://mlsysbook.ai)** by Prof. Vijay Janapa Reddi (Harvard)
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- TinyTorch's companion textbook providing systems perspective
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- Covers production ML engineering, hardware acceleration, deployment
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- **Perfect pairing**: TinyTorch teaches implementation, ML Systems book teaches context
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### Mathematical Foundations
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**[Deep Learning Book](https://www.deeplearningbook.org/)** by Goodfellow, Bengio, Courville
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- Comprehensive theoretical foundations
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- Mathematical background for concepts you'll implement
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- **Use alongside TinyTorch** for deeper understanding
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### Visual Intuition
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**[3Blue1Brown: Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)**
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- Visual explanations of backpropagation, gradient descent, neural networks
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- **Perfect visual complement** to TinyTorch's hands-on implementation
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**[3Blue1Brown: Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)**
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- Geometric intuition for vectors, matrices, transformations
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- **Helpful refresher** for tensor operations and matrix multiplication
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### Python & NumPy
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**[NumPy Quickstart Tutorial](https://numpy.org/doc/stable/user/quickstart.md)**
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- Essential NumPy operations and array manipulation
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- **Review before Module 01** if NumPy is unfamiliar
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---
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## Ready to Begin?
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**If you can:**
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1. ✅ Write a Python class with methods
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2. ✅ Explain what matrix multiplication does
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3. ✅ Debug Python code using print statements
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**Then you're ready to start building!**
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**Not quite there?** Work through the resources above, then return when ready. TinyTorch will still be here, and you'll get more value once foundations are solid.
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---
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## Next Steps
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**Ready to Build:**
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- See [Quick Start Guide](quickstart-guide.md) for hands-on experience
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- See [Student Workflow](student-workflow.md) for development process
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- See [Course Structure](chapters/00-introduction.md) for full curriculum
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**Need More Context:**
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- See [Additional Resources](resources.md) for broader ML learning materials
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- See [FAQ](faq.md) for common questions about TinyTorch
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- See [Community](community.md) to connect with other learners
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---
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**Your journey from ML user to ML systems engineer starts here.**
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