From 246d3359e928f9d160dcb8d255e432719abd4d24 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Fri, 18 Jul 2025 11:13:36 -0400 Subject: [PATCH] Streamline resources page: remove fluff, keep concrete value - 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 --- book/resources.md | 56 ++--------------------------------------------- 1 file changed, 2 insertions(+), 54 deletions(-) diff --git a/book/resources.md b/book/resources.md index 1ffdab3c..29ba996d 100644 --- a/book/resources.md +++ b/book/resources.md @@ -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.* 🚀 \ No newline at end of file +*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.* 🚀 \ No newline at end of file