History

Hemil Desai 820d5c7348 Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR (#4818 ) * Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning * Remove outputs * PR Feedback * some changes * some more changes Co-authored-by: chaton <thomas@grid.ai> Co-authored-by: rohitgr7 <rohitgr1998@gmail.com>		2020-12-10 16:26:18 +05:30
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01-mnist-hello-world.ipynb	add congratulations at the end of our notebooks (#4555 )	2020-11-07 12:05:29 +00:00
02-datamodules.ipynb	add congratulations at the end of our notebooks (#4555 )	2020-11-07 12:05:29 +00:00
03-basic-gan.ipynb	add congratulations at the end of our notebooks (#4555 )	2020-11-07 12:05:29 +00:00
04-transformers-text-classification.ipynb	add congratulations at the end of our notebooks (#4555 )	2020-11-07 12:05:29 +00:00
05-trainer-flags-overview.ipynb	docs: default_root_path -> default_root_dir (#4942 )	2020-12-02 19:17:34 -05:00
06-cifar10-baseline.ipynb	Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR (#4818 )	2020-12-10 16:26:18 +05:30
README.md	Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR (#4818 )	2020-12-10 16:26:18 +05:30

README.md

Lightning Notebooks ⚡

Official Notebooks

You can easily run any of the official notebooks by clicking the 'Open in Colab' links in the table below 😄

Notebook	Description	Colab Link
MNIST Hello World	Train your first Lightning Module on the classic MNIST Handwritten Digits Dataset.
Datamodules	Learn about DataModules and train a dataset-agnostic model on MNIST and CIFAR10.
GAN	Train a GAN on the MNIST Dataset. Learn how to use multiple optimizers in Lightning.
BERT	Fine-tune HuggingFace Transformers models on the GLUE Benchmark
Trainer Flags	Overview of the available Lightning `Trainer` flags
94% Baseline CIFAR10	Establish a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning