Applies k-means clustering

Performs dimensionality reduction

Trains a CNN for image classification

 Tokenizes and processes text using BERT

layer_trainable

requires_grad

grad

train

train_on_batch

fit

compile

evaluate

get_shape()

dim

shape

output_shape

Computes class probabilities using softmax

Creates a convolutional neural network ResNet18

Trains the model on random data

Applies Grad-CAM for prediction explanation

[3, 4]

[0.1, 0.1]

[9, 16]

[6, 8]

[0, 0, 0, 1, 2]

[1, 1, 1, 1, 1]

[-2, -1, 0, 1, 2]

[2, 1, 0, -1, -2]

It will be very large (exploding gradients)

It will be zero (ReLU dead neurons)

It will match torch.ones_like(output)

It will be near zero due to sigmoid saturation

To tune the learning rate

To verify correctness of backpropagation via numerical gradient

To update parameters using gradient descent

To visualize the loss surface

Resize

RandomCrop

Standardize

Normalize