A tiny implementation of PyTorch's autograd engine

Based on Karpathy sensei's micrograd engine, this project implements from scratch, a minimalist neural network library featuring backpropagation through automatic differentiation on a dynamically built DAG. I wanted to take my learnings to the next level so I added a few improvements:

1. Incorporated full support for vector values and mathematical operations like dot product and concat.
2. Convenient methods in the MLP class to predict outputs and train neural networks.

Using tinygrad

Trying out on a tiny dataset

xs = [
    [2, 3, -1],
    [3, -1, 0.5],
    [0.5, 1, 1],
    [1, 1, -1],
]

ys = [1, -1, -1, 1]

Initialising the network

# 3 featured input shape, (5, 4, 1) neural network shape
n = MLP(3, [5, 4, 1])

Making predictions and calculating the RMS loss

predictions = n.predict(xs)
loss_before_training = n.loss(predictions, ys).data
print(f"Loss before training: {loss_before_training} and predictions: {predictions}")

# prints Loss before training: 14.646449126643851 and predictions: [17.067146324690277, 15.92326097064621, 15.238256397218107, 8.059954982580635]

Training the network

loss_after_training = n.train(xs, ys, iterations=1000, lr=0.01)
print(f"Loss after training: {loss_after_training['loss']} and predictions: {loss_after_training['predictions']}")

# prints Loss after training: 0.7102683487043046 and predictions: [1.0597375025781735, 0.0, 0.0, 0.8801837261876291]