LSTM and GRU in PyTorch

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (NIPS 2016) - Tensorflow 1.0

Predict handwritten digits with CoreML

End to End learning for Video Generation from Text

A resource-conscious neural network implementation for MCUs

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

Pytorch mnist example

Tensorflow implementation for 'LCNN: Lookup-based Convolutional Neural Network'. Predict Faster using Models Trained Fast with Multi-GPUs

Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)

Experiments on MNIST dataset and federated training using Flower framework

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

Official adversarial mixup resynthesis repository

Draw and classify digits (0-9) in a browser using machine learning

Implementing Deep learning in R using Keras and Tensorflow packages for R and implementing a Multi layer perceptron Model on MNIST dataset and doing Digit Recognition

Implementing CNN for Digit Recognition (MNIST and SVHN dataset) using PyTorch C++ API

Example C++ CUDA implementation for training Neural Network on MNIST dataset

TensorFlow implementation of "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection"