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A collection of various deep learning architectures, models, and tips
pytorch_ipynb | ||
pytorch-lightning_ipynb | ||
templates/pytorch_lightning | ||
tensorflow1_ipynb | ||
.gitignore | ||
.pep8speaks.yml | ||
LICENSE | ||
README.md |
Deep Learning Models
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
Traditional Machine Learning
Multilayer Perceptrons
Convolutional Neural Networks
Basic
Title | Dataset | Description | Notebooks |
---|---|---|---|
Convolutional Neural Network | TBD | TBD | |
CNN with He Initialization | TBD | TBD |
Concepts
Title | Dataset | Description | Notebooks |
---|---|---|---|
Replacing Fully-Connnected by Equivalent Convolutional Layers | TBD | TBD |
AlexNet
Title | Dataset | Description | Notebooks |
---|---|---|---|
AlexNet Trained on CIFAR-10 | TBD | TBD | |
AlexNet with Grouped Convolutions Trained on CIFAR-10 | TBD | TBD |
DenseNet
Title | Description | Daset | Notebooks |
---|---|---|---|
DenseNet-121 Digit Classifier Trained on MNIST | TBD | TBD | |
DenseNet-121 Image Classifier Trained on CIFAR-10 | TBD | TBD |
Fully Convolutional
Title | Dataset | Description | Notebooks |
---|---|---|---|
"All Convolutionl Net" -- A Fully Convolutional Neural Network | TBD | TBD |
LeNet
Title | Dataset | Description | Notebooks |
---|---|---|---|
LeNet-5 on MNIST | TBD | TBD | |
LeNet-5 on CIFAR-10 | TBD | TBD | |
LeNet-5 on QuickDraw | TBD | TBD |
MobileNet
Title | Dataset | Description | Notebooks |
---|---|---|---|
MobileNet-v2 on Cifar-10 | TBD | TBD | |
MobileNet-v3 small on Cifar-10 | TBD | TBD | |
MobileNet-v3 large on Cifar-10 | TBD | TBD |
Network in Network
Title | Dataset | Description | Notebooks |
---|---|---|---|
Network in Network Trained on CIFAR-10 | TBD | TBD |
VGG
ResNet
Transformers
Title | Dataset | Description | Notebooks |
---|---|---|---|
Multilabel DistilBert | Jigsaw Toxic Comment Challenge | DistilBert fine-tuning |
Ordinal Regression and Deep Learning
Please note that the following notebooks below provide reference implementations to use the respective methods. They are not performance benchmarks.
Title | Dataset | Description | Notebooks |
---|---|---|---|
Baseline multilayer perceptron | Cement | A baseline multilayer perceptron for classification trained with the standard cross entropy loss | |
CORAL multilayer perceptron | Cement | Implementation of Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation 2020 | |
CORN multilayer perceptron | Cement | Implementation of Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities 2022 | |
Binary extension multilayer perceptron | Cement | Implementation of Ordinal Regression with Multiple Output CNN for Age Estimation 2016 | |
Reformulated squared-error multilayer perceptron | Cement | Implementation of A simple squared-error reformulation for ordinal classification 2016 | |
Class distance weighted cross-entropy loss | Cement | Implementation of Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation 2022 |
Normalization Layers
Metric Learning
Title | Dataset | Description | Notebooks |
---|---|---|---|
Siamese Network with Multilayer Perceptrons | TBD | TBD |
Autoencoders
Fully-connected Autoencoders
Title | Dataset | Description | Notebooks |
---|---|---|---|
Autoencoder (MNIST) | TBD | TBD | |
Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier | TBD | TBD |
Convolutional Autoencoders
Variational Autoencoders
Title | Dataset | Description | Notebooks |
---|---|---|---|
Variational Autoencoder | TBD | TBD | |
Convolutional Variational Autoencoder | TBD | TBD |
Conditional Variational Autoencoders
Generative Adversarial Networks (GANs)
Graph Neural Networks (GNNs)
Recurrent Neural Networks (RNNs)
Many-to-one: Sentiment Analysis / Classification
Many-to-Many / Sequence-to-Sequence
Title | Dataset | Description | Notebooks |
---|---|---|---|
A simple character RNN to generate new text (Charles Dickens) | TBD | TBD |
Model Evaluation
K-Fold Cross-Validation
Tips and Tricks
Title | Dataset | Description | Notebooks |
---|---|---|---|
Cyclical Learning Rate | TBD | TBD | |
Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet) | TBD | TBD | |
Gradient Clipping (w. MLP on MNIST) | TBD | TBD |
Transfer Learning
Title | Dataset | Description | Notebooks |
---|---|---|---|
Transfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10) | TBD | TBD |
Visualization and Interpretation
PyTorch Workflows and Mechanics
PyTorch Lightning Examples
Title | Dataset | Description | Notebooks |
---|---|---|---|
MLP in Lightning with TensorBoard -- continue training the last model | TBD | TBD | |
MLP in Lightning with TensorBoard -- checkpointing best model | TBD | TBD |
Custom Datasets
Training and Preprocessing
Improving Memory Efficiency
Title | Dataset | Description | Notebooks |
---|---|---|---|
Gradient Checkpointing Demo (Network-in-Network trained on CIFAR-10) | TBD | TBD |
Parallel Computing
Title | Description | Notebooks |
---|---|---|
Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA | TBD | |
Distribute a Model Across Multiple GPUs with Pipeline Parallelism (VGG-16 Example) | TBD |
Other
Autograd
Title | Dataset | Description | Notebooks |
---|---|---|---|
Getting Gradients of an Intermediate Variable in PyTorch | TBD | TBD |
TensorFlow Workflows and Mechanics
Custom Datasets
Training and Preprocessing
Title | Dataset | Description | Notebooks |
---|---|---|---|
Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives | TBD | TBD |
Related Libraries
Title | Description | Notebooks |
---|---|---|
TorchMetrics | How do we use it, and what's the difference between .update() and .forward()? |