Reimplementing PyTorch from scratch (MLP, CNN) to learn the internals
Implement classic neural networks from scratch without using nn.Module, to deeply understand PyTorch and model principles. - geyuxu/nn-from-scratch
Reimplementing PyTorch from scratch (MLP, CNN) to learn the internals launched on February 3, 2026 with 5 votes, #1,882 of 16,627 launches that month and more than 75% of that year's launches.
What it does
"What I cannot create, I do not understand." — Richard Feynman This project implements classic neural network architectures (MLP, CNN) without using torch.nn.Module . The goal is to demystify the "black box" of Deep Learning. By rebuilding layers like Linear , Conv2d , and MaxPool2d using only raw tensors and Autograd, we can visualize the math behind the magic. # The "Magic" Way import torch . nn as nn model = nn . Linear ( 10 , 1 ) # What actually happens here? This repo does: # The "From Scratch" Way (What we build) import torch class MyLinear : def __init__ ( self , in_features , out_features ): # We manually initialize weights and bias self . weights = torch . randn ( out_features ,…from github.com
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