Name		Name	Last commit message	Last commit date
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.gitignore		.gitignore
README.md		README.md
by_examples.ipynb		by_examples.ipynb
caltech101_data.py		caltech101_data.py
cifar10_classification.ipynb		cifar10_classification.ipynb
dcgan_image_generation.ipynb		dcgan_image_generation.ipynb
debugging.ipynb		debugging.ipynb
dynamic_and_static_nn.ipynb		dynamic_and_static_nn.ipynb
finetuning.py		finetuning.py
graph_converters.ipynb		graph_converters.ipynb
mixed_precision_training.ipynb		mixed_precision_training.ipynb
model_finetuning.ipynb		model_finetuning.ipynb
multi_device_training.ipynb		multi_device_training.ipynb
nbmerge.py		nbmerge.py
python_api.ipynb		python_api.ipynb
siamese_feature_embedding.ipynb		siamese_feature_embedding.ipynb
tiny_digits.py		tiny_digits.py
vae_unsupervised_learning.ipynb		vae_unsupervised_learning.ipynb
vat_semi_supervised_learning.ipynb		vat_semi_supervised_learning.ipynb

README.md

NNabla Tutorial

NNabla's tutorials are provided as Jupyter Notebooks where you can run the code step-by-step.

On many Linux (probably on Windows), Jupyter can be easily installed using pip by:

pip install ipython<=5  # IPython>=6 is not supported in Python<=3.3.
pip install jupyter

You can run Jupyter server on your terminal on this folder.

jupyter notebook

Then, follow the instruction printed on your terminal to connect the server on your browser.

Open the .ipynb files to start tutorials.

You can also run it directly on Colab from the links in the table below.

Name	Notebook	Doc
NNabla By Examples		read
NNabla Python API		read
Cifar-10 Training		Coming Soon
Model Finetuning		read
Debugging		read
Static vs. Dynamic Neural Network in NNabla		read
Image Generation with DCGAN		Coming Soon
Semi-Supervised Learning with VAT		Coming Soon
Unsupervised Learning with VAE		Coming Soon
Feature Embedding with SiameseNet		Coming Soon
Mixed Precision Training		read
Data Parallel Distributed Training		read
Graph Converter for Inference		read