Comments (1)
The issue is not as described, since the MLP is reinitiated ypred = [n(x) for x in xs]
from micrograd.
Related Issues (20)
- Appreciation and seed to GPU Support using PyCuda
- `other` should have a gradient in `__pow__` (?) HOT 2
- micrograd.NET: C# port for .NET developers
- Noob question about backprop implementation HOT 4
- Homework Assignment Error with softmax activation function HOT 1
- Issue with zero_grad? HOT 5
- _backward as lambdas? HOT 3
- Grad should be a Value instead of python/numpy scalar HOT 2
- Sequential MLP implementation
- Zero_grad only zeros the weight and bias nodes, not the nodes for addition and multiplication HOT 1
- A tensor version for micrograd inpired by this work
- Would need 'substraction' support in Engine, mainly for regression loss functions like MSE HOT 4
- Rename engine.py to value.py
- Vectorized implementation with PyTorch flavor
- Vectorized modification with GPU support.
- Another MiniGrad with the RAdam optimizer.
- Ensure backward() is idempotent HOT 4
- Regarding the gradient update of the __sub__ operation HOT 1
- PyPI package HOT 2
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from micrograd.