🧠 How a Deep Learning Framework Actually Works (From-Scratch PyTorch Clone)

What happens under the hood when you call .backward() in PyTorch?
Spoiler: it’s way more magical — and mechanical — than you think.

In my latest post, I walk through building a mini deep learning framework from scratch, inspired by PyTorch.
You’ll learn:

• 📦 How tensors are really stored (hint: it's all flat memory)
• ↔️ How slicing, transposing & reshaping work without copying data
• 🧮 What broadcasting actually does — and how it impacts gradients
• 🔁 The magic behind autograd & computational graphs
• ⚙️ Key optimizations: block matrix multiplication, in-place ops, memory reuse

👉 Read the full deep dive here (code examples included):
🔗 How a Modern Deep Learning Framework Works: Insights from Building a “PyTorch-Like” Library from Scratch

🧑‍🔬 Whether you’re building custom layers, debugging weird gradients, or just curious how deep learning actually works — this will make you a better ML engineer.
Even if you never touch C++ or CUDA, understanding tensor internals helps you:

• Avoid shape mismatch headaches
• Optimize memory & performance
• Write cleaner, faster, more reliable training code