Remove examples from README and replace with links

README.md

@@ -22,76 +22,6 @@ import sys
 sys.path.insert(0, os.path.expanduser("~/micrograd-pp/python"))
 ```
 
-## Example: MNIST
+## Examples
 
-![](https://upload.wikimedia.org/wikipedia/commons/f/f7/MnistExamplesModified.png)
-
-[MNIST](https://en.wikipedia.org/wiki/MNIST_database) is a dataset of handwritten digits (0-9) commonly used for training and testing image processing systems.
-It consists of 28x28 pixel grayscale images, with a total of 60,000 training samples and 10,000 test samples.
-It's widely used in machine learning for digit recognition tasks.
-
-Below is an example of using Micrograd++ to train a simple [feedforward neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network) on MNIST.
-
-```python
-import micrograd_pp as mpp
-import numpy as np
-
-
-batch_sz = 64
-n_epochs = 3
-
-mnist = mpp.datasets.load_mnist(normalize=True)
-train_images, train_labels, test_images, test_labels = mnist
-
-# Flatten images
-train_images = train_images.reshape(-1, 28 * 28)
-test_images = test_images.reshape(-1, 28 * 28)
-
-# Drop extra training examples
-trim = train_images.shape[0] % batch_sz
-train_images = train_images[: train_images.shape[0] - trim]
-
-# Shuffle
-indices = np.random.permutation(train_images.shape[0])
-train_images = train_images[indices]
-train_labels = train_labels[indices]
-
-# Make batches
-n_batches = train_images.shape[0] // batch_sz
-train_images = np.split(train_images, n_batches)
-train_labels = np.split(train_labels, n_batches)
-
-# Optimizer
-opt = mpp.SGD(lr=0.01)
-
-# Feedforward neural network
-model = mpp.Sequential(
-    mpp.Linear(28 * 28, 128),
-    mpp.ReLU(),
-    mpp.Linear(128, 10),
-)
-
-
-def cross_entropy_loss(input_: mpp.Expr, target: mpp.Expr) -> mpp.Expr:
-    n, _ = input_.shape
-    input_max = input_.max(dim=1, keepdim=True)
-    delta = input_ - input_max
-    log_sum_exp = delta.exp().sum(dim=1).log().squeeze()
-    return (log_sum_exp - delta[np.arange(n), target]).sum() / n
-
-
-# Train
-accuracy = float("nan")
-for epoch in range(n_epochs):
-    for batch_index in np.random.permutation(np.arange(n_batches)):
-        x = mpp.Constant(train_images[batch_index])
-        y = train_labels[batch_index]
-        loss = cross_entropy_loss(model(x), y)
-        loss.backward(opt=opt)
-        opt.step()
-    test_x = mpp.Constant(test_images)
-    test_fx = model(test_x)
-    pred_labels = np.argmax(test_fx.value, axis=1)
-    accuracy = (pred_labels == test_labels).mean().item()
-    print(f"Test accuracy at epoch {epoch}: {accuracy * 100:.2f}%")
-```
+* [Train a simple feedforward neural network on MNIST to classify handwritten digits](https://github.com/parsiad/micrograd-pp/blob/main/examples/mnist.ipynb)