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https://github.com/rasbt/deeplearning-models
A collection of various deep learning architectures, models, and tips
https://github.com/rasbt/deeplearning-models
Last synced: 13 days ago
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A collection of various deep learning architectures, models, and tips
- Host: GitHub
- URL: https://github.com/rasbt/deeplearning-models
- Owner: rasbt
- License: mit
- Created: 2019-06-05T16:08:20.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-08T16:35:25.000Z (9 months ago)
- Last Synced: 2024-10-03T09:50:38.568Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 42 MB
- Stars: 16,638
- Watchers: 594
- Forks: 3,997
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome - rasbt/deeplearning-models - A collection of various deep learning architectures, models, and tips (Jupyter Notebook)
- awesome-ai-list-guide - deeplearning-models
README
# Deep Learning Models
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
## Traditional Machine Learning
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Perceptron | 2D toy data | TBD | [](pytorch_ipynb/basic-ml/perceptron.ipynb) [](tensorflow1_ipynb/basic-ml/perceptron.ipynb) |
| Logistic Regression | 2D toy data | TBD | [](pytorch_ipynb/basic-ml/logistic-regression.ipynb) [](tensorflow1_ipynb/basic-ml/logistic-regression.ipynb)|
| Softmax Regression (Multinomial Logistic Regression) | MNIST | TBD | [](pytorch_ipynb/basic-ml/softmax-regression.ipynb) [](tensorflow1_ipynb/basic-ml/softmax-regression.ipynb) |
| Softmax Regression with MLxtend's plot_decision_regions on Iris | Iris | TBD | [](pytorch_ipynb/basic-ml/softmax-regression-mlxtend-1.ipynb) |
## Multilayer Perceptrons
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Multilayer Perceptron | MNIST | TBD | [](pytorch-lightning_ipynb/mlp/mlp-basic.ipynb) [](pytorch_ipynb/mlp/mlp-basic.ipynb) [](tensorflow1_ipynb/mlp/mlp-basic.ipynb) |
| Multilayer Perceptron with Dropout | MNIST | TBD | [](pytorch-lightning_ipynb/mlp/mlp-dropout.ipynb) [](pytorch_ipynb/mlp/mlp-dropout.ipynb) [](tensorflow1_ipynb/mlp/mlp-dropout.ipynb) |
|Multilayer Perceptron with Batch Normalization | MNIST | TBD | [](pytorch-lightning_ipynb/mlp/mlp-batchnorm.ipynb) [](pytorch_ipynb/mlp/mlp-batchnorm.ipynb) [](tensorflow1_ipynb/mlp/mlp-batchtnorm.ipynb) |
|Multilayer Perceptron with Backpropagation from Scratch | MNIST | TBD | [](pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb) [](tensorflow1_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb)|
## Convolutional Neural Networks
#### Basic
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Convolutional Neural Network | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-basic.ipynb) [](pytorch_ipynb/cnn/cnn-basic.ipynb) [](tensorflow1_ipynb/cnn/cnn-basic.ipynb) |
| CNN with He Initialization | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-he-init.ipynb) [](pytorch_ipynb/cnn/cnn-he-init.ipynb) |
#### Concepts
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Replacing Fully-Connected by Equivalent Convolutional Layers | TBD | TBD | [](pytorch_ipynb/cnn/fc-to-conv.ipynb) |
---
#### AlexNet
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| AlexNet Trained on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-alexnet-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb) |
| AlexNet with Grouped Convolutions Trained on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-alexnet-grouped-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-alexnet-grouped-cifar10.ipynb) |
#### DenseNet
|Title | Description | Daset | Notebooks |
| --- | --- | --- | --- |
| DenseNet-121 Digit Classifier Trained on MNIST | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-densenet121-mnist.ipynb) [](pytorch_ipynb/cnn/cnn-densenet121-mnist.ipynb) |
| DenseNet-121 Image Classifier Trained on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-densenet121-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-densenet121-cifar10.ipynb) |
#### Fully Convolutional
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| "All Convolutionl Net" -- A Fully Convolutional Neural Network | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-allconv.ipynb) [](pytorch_ipynb/cnn/cnn-allconv.ipynb) |
#### LeNet
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| LeNet-5 on MNIST | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-lenet5-mnist.ipynb) [](pytorch_ipynb/cnn/cnn-lenet5-mnist.ipynb) |
| LeNet-5 on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-lenet5-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-lenet5-cifar10.ipynb) |
| LeNet-5 on QuickDraw | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-lenet5-quickdraw.ipynb) [](pytorch_ipynb/cnn/cnn-lenet5-quickdraw.ipynb) |
#### MobileNet
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| MobileNet-v2 on Cifar-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb) |
| MobileNet-v3 small on Cifar-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb) |
| MobileNet-v3 large on Cifar-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb) [](pytorch_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb) |
| MobileNet-v3 large on MNIST via Embetter | TBD | TBD | [](pytorch_ipynb/cnn/cnn-embetter-mobilenet.ipynb) |
#### Network in Network
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Network in Network Trained on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-nin-cifar10.ipynb) [](pytorch_ipynb/cnn/nin-cifar10.ipynb) |
#### VGG
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Convolutional Neural Network VGG-16 Trained on CIFAR-10 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-vgg16.ipynb) [](pytorch_ipynb/cnn/cnn-vgg16.ipynb) [](tensorflow1_ipynb/cnn/cnn-vgg16.ipynb) |
| VGG-16 Smile Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch-lightning_ipynb/cnn/cnn-vgg16-celeba.ipynb) [](pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb) |
| VGG-16 Dogs vs Cats Classifier | TBD | TBD | [](pytorch_ipynb/cnn/cnn-vgg16-cats-dogs.ipynb) |
| Convolutional Neural Network VGG-19 | TBD | TBD | [](pytorch-lightning_ipynb/cnn/cnn-vgg19.ipynb) [](pytorch_ipynb/cnn/cnn-vgg19.ipynb) |
#### ResNet
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| ResNet and Residual Blocks | [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [](pytorch_ipynb/cnn/resnet-ex-1.ipynb) |
| ResNet-18 Digit Classifier| [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [](pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb) |
| ResNet-18 Gender Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb) |
| ResNet-34 Digit Classifier | [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [](pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb) |
| ResNet-34 Object Classifier | [QuickDraw](https://quickdraw.withgoogle.com) | TBD | [](pytorch_ipynb/cnn/cnn-resnet34-quickdraw.ipynb) |
| ResNet-34 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb) |
| ResNet-50 Digit Classifier| [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [](pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb) |
| ResNet-50 Gender Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb) |
| ResNet-101 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb) |
| ResNet-101| [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet101-cifar10.ipynb) |
| ResNet-152 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [](pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb) |
---
## Transformers
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Multilabel DistilBERT | [Jigsaw Toxic Comment Challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) | DistilBERT classifier fine-tuning | [](pytorch_ipynb/transformer/distilbert-multilabel.ipynb) |
| DistilBERT as feature extractor | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | DistilBERT classifier with sklearn random forest and logistic regression | [](pytorch_ipynb/transformer/1_distilbert-as-feature-extractor.ipynb) |
| DistilBERT as feature extractor using `embetter` | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | DistilBERT classifier with sklearn random forest and logistic regression using the scikit-learn `embetter` library | [](pytorch_ipynb/transformer/distilbert-embetter-feature-extractor.ipynb) |
| Fine-tune DistilBERT I | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | Fine-tune only the last 2 layers of DistilBERT classifier | [](pytorch-lightning_ipynb/transformer/distilbert-finetune-last-layers.ipynb) |
| Fine-tune DistilBERT II | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | Fine-tune the whole DistilBERT classifier | [](pytorch_ipynb/transformer/distilbert-hf-finetuning.ipynb) [](pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii.ipynb) |
---
## 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 | [](pytorch_ipynb/ordinal/baseline_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/baseline-light_cement.ipynb) |
| CORAL multilayer perceptron | Cement | Implementation of [Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation](https://www.sciencedirect.com/science/article/pii/S016786552030413X) 2020 | [](pytorch_ipynb/ordinal/CORAL_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/CORAL-light_cement.ipynb) |
| CORN multilayer perceptron | Cement | Implementation of [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://arxiv.org/abs/2111.08851) 2022 | [](pytorch_ipynb/ordinal/CORN_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/CORN-light_cement.ipynb) |
| Binary extension multilayer perceptron | Cement | Implementation of [Ordinal Regression with Multiple Output CNN for Age Estimation](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf) 2016 | [](pytorch_ipynb/ordinal/niu2016_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/niu2016-light_cement.ipynb) |
| Reformulated squared-error multilayer perceptron | Cement | Implementation of [A simple squared-error reformulation for ordinal classification](https://arxiv.org/abs/1612.00775) 2016 | [](pytorch_ipynb/ordinal/beckham2016_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/beckham2016-light_cement.ipynb) |
| Class distance weighted cross-entropy loss | Cement | Implementation of [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://arxiv.org/abs/2202.05167) 2022 | [](pytorch_ipynb/ordinal/polat2022_cement.ipynb) [](pytorch-lightning_ipynb/ordinal/polat2022-light_cement.ipynb) |
---
## Normalization Layers
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier | TBD | TBD | [](pytorch_ipynb/cnn/nin-cifar10_batchnorm.ipynb) |
| Filter Response Normalization for Network-in-Network CIFAR-10 Classifier | TBD | TBD | [](pytorch_ipynb/cnn/nin-cifar10_filter-response-norm.ipynb) |
## Metric Learning
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Siamese Network with Multilayer Perceptrons | TBD | TBD | [](tensorflow1_ipynb/metric/siamese-1.ipynb) |
## Autoencoders
#### Fully-connected Autoencoders
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Autoencoder (MNIST) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-basic.ipynb) [](tensorflow1_ipynb/autoencoder/ae-basic.ipynb) |
| Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb) [](tensorflow1_ipynb/autoencoder/ae-basic-with-rf.ipynb) |
#### Convolutional Autoencoders
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Convolutional Autoencoder with Deconvolutions / Transposed Convolutions | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-deconv.ipynb) [](tensorflow1_ipynb/autoencoder/ae-deconv.ipynb) |
| Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-deconv-jaccard.ipynb) |
| Convolutional Autoencoder with Deconvolutions (without pooling operations) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb) |
| Convolutional Autoencoder with Nearest-neighbor Interpolation | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb) [](tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb) |
| Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb) |
| Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb) |
#### Variational Autoencoders
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Variational Autoencoder | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-var.ipynb) |
| Convolutional Variational Autoencoder | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-conv-var.ipynb) |
#### Conditional Variational Autoencoders
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Conditional Variational Autoencoder (with labels in reconstruction loss) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-cvae.ipynb) |
| Conditional Variational Autoencoder (without labels in reconstruction loss) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb) |
| Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb) |
| Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss) | TBD | TBD | [](pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb) |
## Generative Adversarial Networks (GANs)
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Fully Connected GAN on MNIST | TBD | TBD | [](pytorch_ipynb/gan/gan.ipynb) [](tensorflow1_ipynb/gan/gan.ipynb) |
| Fully Connected Wasserstein GAN on MNIST | TBD | TBD | [](pytorch_ipynb/gan/wgan-1.ipynb) |
| Convolutional GAN on MNIST | TBD | TBD | [](pytorch_ipynb/gan/gan-conv.ipynb) [](tensorflow1_ipynb/gan/gan-conv.ipynb) |
| Convolutional GAN on MNIST with Label Smoothing | TBD | TBD | [](pytorch_ipynb/gan/gan-conv-smoothing.ipynb) [](tensorflow1_ipynb/gan/gan-conv-smoothing.ipynb) |
| Convolutional Wasserstein GAN on MNIST | TBD | TBD | [](pytorch_ipynb/gan/dc-wgan-1.ipynb) |
| Deep Convolutional GAN (DCGAN) on Cats and Dogs Images | TBD | TBD | [](pytorch_ipynb/gan/dcgan-cats-and-dogs.ipynb) |
| Deep Convolutional GAN (DCGAN) on CelebA Face Images | TBD | TBD | [](pytorch_ipynb/gan/dcgan-celeba.ipynb) |
## Graph Neural Networks (GNNs)
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Most Basic Graph Neural Network with Gaussian Filter on MNIST | TBD | TBD | [](pytorch_ipynb/gnn/gnn-basic-1.ipynb) |
| Basic Graph Neural Network with Edge Prediction on MNIST | TBD | TBD | [](pytorch_ipynb/gnn/gnn-basic-edge-1.ipynb) |
| Basic Graph Neural Network with Spectral Graph Convolution on MNIST | TBD | TBD | [](pytorch_ipynb/gnn/gnn-basic-graph-spectral-1.ipynb) |
## Recurrent Neural Networks (RNNs)
#### Many-to-one: Sentiment Analysis / Classification
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| A simple single-layer RNN (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_simple_imdb.ipynb) |
| A simple single-layer RNN with packed sequences to ignore padding characters (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb) |
| RNN with LSTM cells (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb) |
| RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors | TBD | TBD | [](pytorch_ipynb/rnn/rnn_lstm_packed_imdb-glove.ipynb) |
| RNN with LSTM cells and Own Dataset in CSV Format (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb) |
| RNN with GRU cells (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb) |
| Multilayer bi-directional RNN (IMDB) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_lstm_bi_imdb.ipynb) |
| Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News) | TBD | TBD | [](pytorch_ipynb/rnn/rnn_bi_multilayer_lstm_own_csv_agnews.ipynb) |
#### Many-to-Many / Sequence-to-Sequence
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| A simple character RNN to generate new text (Charles Dickens) | TBD | TBD | [](pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb) |
## Model Evaluation
### K-Fold Cross-Validation
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Baseline CNN | MNIST | A simple baseline with traditional train/validation/test splits | [](pytorch_ipynb/kfold/baseline-cnn-mnist.ipynb) [](pytorch-lightning_ipynb/kfold/baseline-light-cnn-mnist.ipynb) |
| K-fold with `pl_cross` | MNIST | A 5-fold cross-validation run using the `pl_cross` library | [](pytorch-lightning_ipynb/kfold/kfold-light-cnn-mnist.ipynb) |
## Data Augmentation
| Title | Dataset | Description | Notebooks |
| -------------------------- | ------- | ----------- | ------------------------------------------------------------ |
| AutoAugment & TrivialAugment for Image Data | CIFAR-10 | Trains a ResNet-18 using AutoAugment and TrivialAugment | [](./pytorch-lightning_ipynb/data-augmentation/autoaugment) |
## Tips and Tricks
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Cyclical Learning Rate | TBD | TBD | [](pytorch_ipynb/tricks/cyclical-learning-rate.ipynb) |
| Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet) | TBD | TBD | [](pytorch_ipynb/tricks/cnn-alexnet-cifar10-batchincrease.ipynb) |
| Gradient Clipping (w. MLP on MNIST) | TBD | TBD | [](pytorch_ipynb/tricks/gradclipping_mlp.ipynb) |
## Transfer Learning
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Transfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10) | TBD | TBD | [](pytorch_ipynb/transfer/transferlearning-vgg16-cifar10-1.ipynb) |
## Visualization and Interpretation
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Vanilla Loss Gradient (wrt Inputs) Visualization (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images) | TBD | TBD | [](pytorch_ipynb/viz/cnns/cats-and-dogs/cnn-viz-grad__vgg16-cats-dogs.ipynb) |
| Guided Backpropagation (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images) | TBD | TBD | [](pytorch_ipynb/viz/cnns/cats-and-dogs/cnn-viz-guided-backprop__vgg16-cats-dogs.ipynb) |
## PyTorch Workflows and Mechanics
#### PyTorch Lightning Examples
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| MLP in Lightning with TensorBoard -- continue training the last model | TBD | TBD | [](pytorch_ipynb/lightning/lightning-mlp.ipynb) |
| MLP in Lightning with TensorBoard -- checkpointing best model | TBD | TBD | [](pytorch_ipynb/lightning/lightning-mlp-best-model) |
#### Custom Datasets
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Custom Data Loader Example for PNG Files | TBD | TBD | [](pytorch_ipynb/mechanics/custom-dataloader-png/custom-dataloader-example.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5 | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-csv.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-svhn.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD) | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-afad.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader_dating-historical-color-images.ipynb) |
| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Fashion MNIST | TBD | TBD | [](pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb) |
#### Training and Preprocessing
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| PyTorch DataLoader State and Nested Iterations | Toy | Explains DataLoader behavior when in nested functions | [](pytorch_ipynb/mechanics/dataloader-nesting.ipynb)|
| Generating Validation Set Splits | TBD | TBD | [](pytorch_ipynb/mechanics/validation-splits.ipynb) |
| Dataloading with Pinned Memory | TBD | TBD | [](pytorch_ipynb/cnn/cnn-resnet34-cifar10-pinmem.ipynb) |
| Standardizing Images | TBD | TBD | [](pytorch_ipynb/cnn/cnn-standardized.ipynb) |
| Image Transformation Examples | TBD | TBD | [](pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb) |
| Char-RNN with Own Text File | TBD | TBD | [](pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb) |
| Sentiment Classification RNN with Own CSV File | TBD | TBD | [](pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb) |
#### Improving Memory Efficiency
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Gradient Checkpointing Demo (Network-in-Network trained on CIFAR-10) | TBD | TBD | [](pytorch_ipynb/mechanics/gradient-checkpointing-nin.ipynb) |
#### Parallel Computing
|Title | Description | Notebooks |
| --- | --- | --- |
| Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA | TBD | [](pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb) |
| Distribute a Model Across Multiple GPUs with Pipeline Parallelism (VGG-16 Example) | TBD | [](pytorch_ipynb/mechanics/model-pipeline-vgg16.ipynb) |
#### Other
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| PyTorch with and without Deterministic Behavior -- Runtime Benchmark | TBD | TBD | [](pytorch_ipynb/mechanics/deterministic_benchmark.ipynb) |
| Sequential API and hooks | TBD | TBD | [](pytorch_ipynb/mechanics/mlp-sequential.ipynb) |
| Weight Sharing Within a Layer | TBD | TBD | [](pytorch_ipynb/mechanics/cnn-weight-sharing.ipynb) |
| Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib | TBD | TBD | [](pytorch_ipynb/mechanics/plot-jupyter-matplotlib.ipynb) |
#### Autograd
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Getting Gradients of an Intermediate Variable in PyTorch | TBD | TBD | [](pytorch_ipynb/mechanics/manual-gradients.ipynb) |
## TensorFlow Workflows and Mechanics
#### Custom Datasets
|Title | Description | Notebooks |
| --- | --- | --- |
| Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives | TBD | [](tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb) |
| Storing an Image Dataset for Minibatch Training using HDF5 | TBD | [](tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb) |
| Using Input Pipelines to Read Data from TFRecords Files | TBD | [](tensorflow1_ipynb/mechanics/tfrecords.ipynb) |
| Using Queue Runners to Feed Images Directly from Disk | TBD | [](tensorflow1_ipynb/mechanics/file-queues.ipynb) |
| Using TensorFlow's Dataset API | TBD | [](tensorflow1_ipynb/mechanics/dataset-api.ipynb) |
#### Training and Preprocessing
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives | TBD | TBD | [](tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb) |
## Related Libraries
|Title | Description | Notebooks |
| --- | --- | --- |
| TorchMetrics | How do we use it, and what's the difference between .update() and .forward()? | [](pytorch_ipynb/related-libraries/torchmetrics-update-forward.ipynb) |