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Streamline resources page: remove fluff, keep concrete value

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- Remove generic learning communities section
- Remove vague 'next steps' career advice
- Remove fluffy usage instructions
- Keep focused: academic courses, books, alternative implementations, production internals
- Result: curated reference for students who built ML systems from scratch
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Vijay Janapa Reddi
2025-07-18 11:13:36 -04:00
parent 5f1d74c39c
commit 246d3359e9
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@@ -64,7 +64,7 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
---
## 🏭 **Production Tools & Platforms**
## 🏭 **Production Internals**
### **Framework Deep Dives**
- **[PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/)** by Edward Yang
@@ -73,58 +73,6 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
- **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.html)**
*Custom operators and autograd functions - apply your TinyTorch knowledge*
### **Development Tools**
- **[Papers With Code](https://paperswithcode.com/)**
*Research papers with implementation code - apply your skills to reproduce results*
- **[Weights & Biases](https://wandb.ai/)**
*Experiment tracking and model management - scale your TinyTorch training*
---
## 🌐 **Learning Communities**
### **Technical Discussion**
- **[r/MachineLearning](https://www.reddit.com/r/MachineLearning/)**
*Research discussions and paper releases*
- **[The Gradient](https://thegradient.pub/)**
*Deep technical articles on ML research and systems*
- **[Distill.pub](https://distill.pub/)**
*Interactive explanations of ML concepts with beautiful visualizations*
---
## 🎯 **Next Steps After TinyTorch**
### **Apply Your Skills**
1. **Reproduce Research**: Use your TinyTorch foundation to implement papers from scratch
2. **Contribute to Open Source**: PyTorch, TensorFlow, JAX - you now understand the internals
3. **Build Production Systems**: Apply MLOps principles from your final modules
4. **Optimize for Edge**: Use compression and kernel techniques for deployment
### **Advanced Specializations**
- **Distributed Training**: Scale your framework knowledge to multi-GPU systems
- **Compiler Design**: Build domain-specific languages for ML (JAX, Triton style)
- **Hardware Acceleration**: Custom kernels and specialized processors
- **Systems Research**: Novel architectures and training techniques
---
## 💡 **How to Use These Resources**
```{admonition} 🎯 Strategic Learning Path
:class: tip
**Parallel Learning**: Use these alongside TinyTorch modules for broader context
**Post-TinyTorch**: After completing the framework, dive into production systems
**Compare & Contrast**: Study alternative implementations to understand design trade-offs
```
**Remember**: You now have the implementation foundation that most ML engineers lack. These resources help you apply that knowledge to broader systems and production environments.
---
*Building ML systems from scratch gives you superpowers. These resources help you use them wisely.* 🚀
*Building ML systems from scratch gives you the implementation foundation most ML engineers lack. These resources help you apply that knowledge to broader systems and production environments.* 🚀