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What is this?
A large collection of Jupyter Notebooks implementing various deep learning architectures, models, and training tips for TensorFlow and PyTorch.
A large collection of Jupyter Notebooks implementing various deep learning architectures, models, and training tips for TensorFlow and PyTorch.Is this a library I import, or a set of example notebooks?It is a reference collection of standalone Jupyter notebooks, not an installable library — each notebook is a self-contained implementation you read and run, not a package you import.
It is a reference collection of standalone Jupyter notebooks, not an installable library — each notebook is a self-contained implementation you read and run, not a package you import.How do I use these notebooks?Clone the repo and open the notebook for the model you want in Jupyter; each notebook is runnable on its own and typically trains on a standard dataset like MNIST, CIFAR-10, or CelebA.
Clone the repo and open the notebook for the model you want in Jupyter; each notebook is runnable on its own and typically trains on a standard dataset like MNIST, CIFAR-10, or CelebA.Do I need both TensorFlow and PyTorch installed?You only need whichever framework a given notebook uses — the collection spans both TensorFlow and PyTorch notebooks, so requirements depend on the specific notebook you open.
You only need whichever framework a given notebook uses — the collection spans both TensorFlow and PyTorch notebooks, so requirements depend on the specific notebook you open.Are all the notebooks fully filled in and documented?No — many table entries for dataset, description, and notebook link are still marked as placeholders (TBD), so coverage and documentation quality vary across entries.
No — many table entries for dataset, description, and notebook link are still marked as placeholders (TBD), so coverage and documentation quality vary across entries.Who is this collection meant for?Students and practitioners who want reference implementations of classic and modern deep learning architectures (CNNs, RNNs, GANs, VAEs, GNNs, Transformers) to learn from or adapt.
Students and practitioners who want reference implementations of classic and modern deep learning architectures (CNNs, RNNs, GANs, VAEs, GNNs, Transformers) to learn from or adapt.What model families are covered in the collection?It covers traditional ML (perceptron, logistic/softmax regression), MLPs, CNNs (AlexNet, VGG, ResNet, DenseNet, MobileNet, LeNet), Transformers (DistilBERT), RNNs, autoencoders, VAEs, GANs, and graph neural networks (GNNs).
It covers traditional ML (perceptron, logistic/softmax regression), MLPs, CNNs (AlexNet, VGG, ResNet, DenseNet, MobileNet, LeNet), Transformers (DistilBERT), RNNs, autoencoders, VAEs, GANs, and graph neural networks (GNNs).Are the ordinal regression notebooks meant as benchmarks?No, they are explicitly reference implementations of papers like CORAL and CORN ordinal regression methods, not performance benchmarks.
No, they are explicitly reference implementations of papers like CORAL and CORN ordinal regression methods, not performance benchmarks.Does it include PyTorch Lightning examples?Yes, there are PyTorch Lightning notebooks demonstrating checkpointing and continued training with TensorBoard logging.
Yes, there are PyTorch Lightning notebooks demonstrating checkpointing and continued training with TensorBoard logging.What datasets are commonly used across the notebooks?MNIST, CIFAR-10, CelebA, IMDB movie reviews, and QuickDraw are the most frequently used datasets.
MNIST, CIFAR-10, CelebA, IMDB movie reviews, and QuickDraw are the most frequently used datasets.What are similar projects?DeepLearning, AI-For-Beginners, leedl-tutorial, pytorch-book, TensorFlow-Examples, fastbook, nlp-tutorial, DeepLearning-500-questions
DeepLearning, AI-For-Beginners, leedl-tutorial, pytorch-book, TensorFlow-Examples, fastbook, nlp-tutorial, DeepLearning-500-questionsWhich topics is it in?rnn, transfer-learning, convolutional-neural-networks, gan, tensorflow, natural-language-processing, neural-network, jupyter notebook, transformer, computer-vision, pytorch, tutorial
rnn, transfer-learning, convolutional-neural-networks, gan, tensorflow, natural-language-processing, neural-network, jupyter notebook, transformer, computer-vision, pytorch, tutorial