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minigrad's Introduction

mini-grad

Inspired by : GeoHot's TinyGrad

Learning how to make a custom deep learning framework using only numpy. Mostly, for now I will try to understand what George did in his work and recreate the same results. Hopefully, I will be able to add something new to it. If not, I will have at least learned something.

Example

import numpy as np
from minigrad.tensor import Tensor

x = Tensor(np.eye(3))
y = Tensor(np.array([[2.0,0,-2.0]]))
z = y.dot(x).sum()
z.backward()

print(x.grad)  # dz/dx
print(y.grad)  # dz/dy

Same example in torch

import torch

x = torch.eye(3, requires_grad=True)
y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
z = y.matmul(x).sum()
z.backward()

print(x.grad)  # dz/dx
print(y.grad)  # dz/dy

Neural Networks ??

It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an optimizer (SGD and Adam implemented) from minigrad.optim, write some boilerplate minibatching code, and you have all you need.

TODO (to make real neural network library)

  • Look into Adam Optimizer
  • Implement convolutions

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