Deep-Learning-ModelsDeep Learning Models implemented in python.
Stars: ✭ 17 (-66.67%)
Mutual labels: restricted-boltzmann-machine, rbm
Generative ModelsCollection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Stars: ✭ 6,701 (+13039.22%)
Mutual labels: restricted-boltzmann-machine, rbm
NNetalgorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network
Stars: ✭ 24 (-52.94%)
Mutual labels: restricted-boltzmann-machine
Handwritten-Digits-Classification-Using-KNN-Multiclass Perceptron-SVM🏆 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.
Stars: ✭ 42 (-17.65%)
Mutual labels: mnist-dataset
amrOfficial adversarial mixup resynthesis repository
Stars: ✭ 31 (-39.22%)
Mutual labels: mnist-dataset
mnist-drawDraw and classify digits (0-9) in a browser using machine learning
Stars: ✭ 27 (-47.06%)
Mutual labels: mnist-dataset
MNIST-CoreMLPredict handwritten digits with CoreML
Stars: ✭ 63 (+23.53%)
Mutual labels: mnist-dataset
mnist-challengeMy solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
Stars: ✭ 68 (+33.33%)
Mutual labels: rbm
CVAE-AnomalyDetection-PyTorchExample of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)
Stars: ✭ 23 (-54.9%)
Mutual labels: mnist-dataset
DBNSimple code tutorial for deep belief network (DBN)
Stars: ✭ 34 (-33.33%)
Mutual labels: restricted-boltzmann-machine
dltfHands-on in-person workshop for Deep Learning with TensorFlow
Stars: ✭ 14 (-72.55%)
Mutual labels: rbm
Hand-Digits-RecognitionRecognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.
Stars: ✭ 11 (-78.43%)
Mutual labels: mnist-dataset
Deep-Learning-in-R-using-Keras-and-Tensorflow-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
Stars: ✭ 24 (-52.94%)
Mutual labels: mnist-dataset
Handwritten-Names-RecognitionThe goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
Stars: ✭ 54 (+5.88%)
Mutual labels: restricted-boltzmann-machine
tensorflow-rbmTensorflow implementation of the Restricted Boltzmann Machine
Stars: ✭ 308 (+503.92%)
Mutual labels: rbm
MNIST-cnnConvolutional neural networks with Python 3
Stars: ✭ 19 (-62.75%)
Mutual labels: mnist-dataset
videoMultiGANEnd to End learning for Video Generation from Text
Stars: ✭ 53 (+3.92%)
Mutual labels: mnist-dataset
learnergy💡 Learnergy is a Python library for energy-based machine learning models.
Stars: ✭ 57 (+11.76%)
Mutual labels: rbm
MNISTHandwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
Stars: ✭ 28 (-45.1%)
Mutual labels: mnist-dataset
