# Deep Learning Resources

## Neural Networks and Deep Learning Model Zoo

A collection of standalone TensorFlow and PyTorch models in Jupyter Notebooks

![Python 3.6](https://img.shields.io/badge/Python-3.6-blue.svg)

**Classifiers**

* Perceptron \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/perceptron.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/perceptron.ipynb)]
* Logistic Regression \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/logistic-regression.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/logistic-regression.ipynb)]
* Softmax Regression (Multinomial Logistic Regression) \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/softmax-regression.ipynb)]\[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/softmax-regression.ipynb)]
* Multilayer Perceptron \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/multilayer-perceptron.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/multilayer-perceptron.ipynb)]
* Multilayer Perceptron with Dropout \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/multilayer-perceptron-dropout.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/multilayer-perceptron-dropout.ipynb)]
* Multilayer Perceptron with Batch Normalization \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/multilayer-perceptron-batchnorm.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/multilayer-perceptron-batchnorm.ipynb)]
* Multilayer Perceptron with Backpropagation from Scratch \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/multilayer-perceptron-lowlevel.ipynb)]

**Convolutional Classifiers**

* Convolutional Neural Network \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/convnet.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/convnet.ipynb)]
* Convolutional Neural Network with He Initialization \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/convnet-he-init.ipynb)]
* Convolutional Neural Network VGG-16 \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/convnet-vgg16.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/convnet-vgg16.ipynb)]
* Convolutional ResNet and Residual Blocks \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/resnet-ex-1.ipynb)]

**Metric Learning**

* Siamese Network with Multilayer Perceptrons \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/siamese-1.ipynb)]

**Autoencoders**

* Autoencoder \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/autoencoder.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder.ipynb)]
* Convolutional Autoencoder with Deconvolutions \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/autoencoder-deconv.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder-deconv.ipynb)]
* Convolutional Autoencoder with Deconvolutions (without pooling operations) \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder-deconv-2.ipynb)]
* Convolutional Autoencoder with Nearest-neighbor Interpolation \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/autoencoder-conv.ipynb)] \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder-conv.ipynb)]
* Convolutional Autoencoder with Nearest-neighbor Interpolation – Trained on CelebA \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder-conv-2.ipynb)]
* Variational Autoencoder \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/autoencoder-var.ipynb)]

**General Adversarial Networks**

* General Adversarial Networks \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/gan.ipynb)]
* Convolutional General Adversarial Networks \[[TensorFlow](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/gan-conv.ipynb)]

**Tips and Tricks**

* Cyclic Learning Rate \[[PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/cyclical-learning-rate.ipynb)]

**PyTorch Workflows**

* [Using PyTorch Dataset Loading Utilities for Custom Datasets – CSV files converted to HDF5](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/custom-data-loader-csv.ipynb)
* [Using PyTorch Dataset Loading Utilities for Custom Datasets – Face Images from CelebA](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/custom-data-loader-celeba.ipynb)
* [Getting Gradients of an Intermediate Variable in PyTorch](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/pytorch_ipynb/manual-gradients.ipynb)

**TensorFlow Workflows**

* [Saving and Loading Trained Models – from TensorFlow Checkpoint Files and NumPy NPZ Archives](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/saving-and-reloading-models.ipynb)
* [Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/image-data-chunking-npz.ipynb)
* [Storing an Image Dataset for Minibatch Training using HDF5](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/image-data-chunking-hdf5.ipynb)
* [Using Input Pipelines to Read Data from TFRecords Files](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/tfrecords.ipynb)
* [Using Queue Runners to Feed Images Directly from Disk](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/file-queues.ipynb)
* [Using TensorFlow’s Dataset API](https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/tensorflow_ipynb/dataset-api.ipynb)

## Visual Neural Network

{% embed url="<http://scs.ryerson.ca/~aharley/vis/conv/flat.html>" %}

{% embed url="<https://playground.tensorflow.org>" %}