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This gallery of examples uses luz to train and validate a range of common deep learning architectures. The gallery also demonstrates basic and advanced usage of luz.

CharGPT
intermediate

Train a character-level GPT-2 on Shakespeare texts.

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Binary classification
basic

Demonstrates using pre-trained models to build a binary classification model.

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Autoencoder
basic

Builds an autoencoder for the MNIST dataset. Demonstrates overwriting the predict method

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Virtual batch size
advanced

Showcases how to create a custom fully customized training step

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Simple CNN
basic

Trains an AlexNet-like CNN to classify digits of the MNIST dataset

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DCGAN
intermediate

Deep Convolutional Generative Adversarial Network to generate images.

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MixUp augmentation
intermediate

Demonstrates using the MixUp callback

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Triplet loss
intermediate

Train an embedding model to minimized the triplet loss.

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UNET implementation
intermediate

Implements a UNET model to separate the background of images of cats and dogs.

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Text classification from scratch
basic

Implements text classification from scratch.

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Training a causal language model from scratch
advanced

Implements datasets and trains a causal language model from scratch using R source code.

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