# Building a Deep Learning Framework from Scratch > Source: > Published: 2026-07-23 16:46:19+00:00 # Building a Deep Learning Framework from Scratch `.backward()` as a black box, the only way is to build the engine yourself.I've found that the gap between reading a paper and actually deploying a model is usually filled with "hidden" assumptions. When you implement a tensor engine from scratch, those assumptions disappear. You stop guessing why a gradient vanished and start seeing exactly where the math broke. For anyone looking for a real-world deep dive, I'm mapping out a 3-5 month trajectory to go from zero to a "Tiny Transformer." This isn't about copy-pasting a library; it's about synthesizing a working system. ## The Learning Path If you're planning this deployment of your own skills, here is the architectural roadmap: 1. **The Autodiff Engine:** Implement a scalar-based automatic differentiation system. You need to track every operation and store the gradient function. 2. **The Tensor Engine:** Move from scalars to multi-dimensional arrays. This is where you'll struggle with broadcasting rules—the most frustrating but essential part of the process. 3. **Neural Network Primitives:** Build the Linear layer and activation functions (ReLU, Sigmoid) using your tensor engine. 4. **The Optimizer:** Implement SGD and Adam from scratch to see how weights actually update based on the gradients you've calculated. 5. **The Tiny Transformer:** The final boss. Combining everything to build a sequence-to-sequence model. ## Why this beats standard tutorials Most beginners just import `torch` and call it a day. That's fine for application, but for true prompt engineering or LLM agent architecture, you need to understand the underlying flow. Comparing a hand-built framework to a production one reveals exactly why things like GPU acceleration and memory management are so critical. For inspiration on the minimal viable product (MVP) of a framework, I recommend studying these specific implementations: ``` https://github.com/karpathy/micrograd https://github.com/tinygrad/tinygrad ``` The goal isn't to reinvent the wheel, but to understand the physics of the wheel. Once you've manually flowed gradients through a graph, you'll have a level of intuition that no textbook can provide. [Next RAG Cost Analysis: Where the Money Actually Goes →](/en/threads/2422/) ## All Replies （4） [@MicroPanda](/en/users/MicroPanda/)Imagine thinking you'd actually enjoy debugging dimension mismatches for three hours straight. absolute madness lol