Neural Networks with Python on the Web

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Feedforward NN
Reccurent NN
Deep Belief Network
Convolutional Neural Network
Recurrent neural network toolbox for Python and Matlab
LSTM Recurrent Neural Network
Convolutional Neural Network and RNN
neural network library for python
Generative Adversarial Networks (GAN)
Spiking Neural Netorks (SNN)
Self-Organising Maps (SOM)
TensorFlow Neural Network
Keras Neural Network
Echo Recurrent Neural Network
Multi-Layer Perceptron
NN TypeFeaturesLibrary usedApplicationLinkAdded on
Feedforward NNSigmoid activation function, grad descent methodtheano theano.tensor theano.tensor.nnetXOR problemBeginner Tutorial: Neural Nets in Theano2016-10-10

Feedforward NNL1 and L2 Regularization
Stochastic gradient descent optimization
sigmoidal (or tanh) activation function
1 hidden layer
theano theano.tensorMNIST digit classificationMultilayer Perceptron2016-10-10
Reccurent NNBackpropagation through time (grad descent)
graph output for error
theano theano.tensor cPickleComputing the dot product of two vectorsImplementing a recurrent neural network in python2016-10-10
Feedforward NNBackpropogation algorithm is using Adam, as efficient variant of Gradient Descent
1 hidden layer with input units = 28*28, training and testing on subset of images.
TensorFlowimage recognition, to identify digits from a given 28 x 28 imageAn Introduction to Implementing Neural Networks using TensorFlow2016-10-10
Deep Belief NetworkUses series of hidden layers
each hidden layer is an unsupervised Restricted Boltzmann Machine.

The output of each RBM in the hidden layer sequence is used as input to the next.
The final hidden layer then connects to an output layer.
sklearn nolearn.dbnto classify images from the MNIST datasetGetting Started with Deep Learning and Python2016-10-10
Feedforward NNBackpropagation algorithm with very short python implementation.
Sigmoid activation function.
numpyPredicting column Y based on input columns X, similar like in XOR operationA Neural Network in 11 lines of Python A bare bones neural network implementation to describe the inner workings of backpropagation.2016-10-10
Convolutional Neural Network2 step training for an image classification problem:
Feature Extraction: extracting new features.
Model Training: utilizing a clean dataset composed of the images features and the corresponding labels.
Transfer learning also used for training convolutional neural networks
CaffeCat/dog image classifier. The dataset is from Kaggle and is comprised of 25,000 images of dogs and cats.A Practical Introduction to Deep Learning with Caffe and Python2016-10-10
Feedforward NNStochastic gradient descent learning algorithm.
Gradients are calculated using backpropagation.
numpyimage classification to recognize handwritten digitsUsing neural nets to recognize handwritten digits2016-10-10
Convolutional Neural NetworkSupports several layer types
(fully connected, convolutional, max pooling, softmax),

and activation functions (sigmoid, tanh, and rectified linear units, with more easily added).

Can run on CPU/GPU
theano theano.tensor cPickle gzipimage classificationDeep learning2016-10-10
Feedforward NNGradient descent, backpropogation, 3 layers (1 input, 1 output, 1 hidden layer)sklearnData classificationIMPLEMENTING A NEURAL NETWORK FROM SCRATCH IN PYTHON ? AN INTRODUCTION2016-10-10
Recurrent neural network toolbox for Python and MatlabLevenberg Marquardt algorithm
(a second-order Quasi-Newton optimization method) for training,
which is much faster than first-order methods like gradient descent.

Real-Time Recurrent Learning (RTRL) algorithm and Backpropagation Through Time (BPTT) algorithm
are implemented
pyrennpyrenn allows to create a wide range of (recurrent) neural network configurations, examples also include feed forward neural netA Recurrent Neural Network Toolbox for Python and MatlabPyrenn2016-10-10
LSTM Recurrent Neural NetworkLong Short-Term Memory Network (LSTM), deep network architecture using stacked LSTM networksKeras, sklearnTime series predictionTime Series Prediction with LSTM Recurrent Neural Networks in Python with Keras2016-10-10
LSTM Recurrent Neural NetworkLong Short-Term Memory Network (LSTM), naive LSTM networkKerassequence prediction problem of learning the alphabet. Given a letter of the alphabet, predict the next letter of the alphabet.Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras2016-10-10
LSTM Recurrent Neural NetworkLong Short-Term Memory Network (LSTM), one or two hidden LSTM layers, dropout, the output layer is a Dense layer using the softmax activation function,
DAM optimization algorithm is used for speed
KerasText Generation. Generation new sequences of characters.Text Generation With LSTM Recurrent Neural Networks in Python with Keras2016-10-10
LSTM Recurrent Neural NetworkLong Short-Term Memory Network (LSTM),
Various layers are used:
Embedded layer for representing each word, Dropout Layer,
one-dimensional CNN and max pooling layers, LSTM layer, Dense output layer with a single neuron and a sigmoid activation.

Log loss is used as the loss function (binary_crossentropy in Keras).

ADAM optimization
KerasSequence classification.Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras2016-10-10
Feedforward NNTwo hidden layers Softmax activation function Model is trained using Stochastic Gradient Descent (SGD)Keras, sklearn.preprocessing, sklearn.cross_validationImage classificationA simple neural network with Python and Keras2016-10-10
Convolutional Neural NetworkTransfer Learning
gradient descent
Image recognition, to identify digits from a given 28 x 28 image.Fine-tuning a Keras model using Theano trained Neural Network & Introduction to Transfer Learning2016-10-10
Convolutional Neural NetworkConvolutional Neural Networks (CNNs) pre-trained on the ImageNet dataset.Keras
image classificationimage classification with Python and Keras2016-10-10
Convolutional Neural NetworkDROPOUT LAYER
implementation of summary to track of and visualize various quantities during training and evaluation
Convolutional Neural NetworkCNN is using smaller network parameters, Word2Vec is trained on taining datasetKeras
Sentence ClassificationConvolutional Neural Networks for Sentence Classification2016-10-10
Convolutional Neural NetworkDropout regularizationmxnetsentence sentiment classificationText Classification Using a Convolutional Neural Network on MXNet2016-10-10
Convolutional Neural Network and RNNRecurrent Neural Networks (LSTM, GRU, Attentional RNN), Convolutional Neural NetworksKerasQuestion AnsweringDeep Language Modeling for Question Answering using Keras2016-10-10
Convolutional Neural NetworkExtracting feature vectors from different layers of CNNCaffeExtract Feature Vectors using CNNHow To Extract Feature Vectors From Deep Neural Networks In Python Caffe2016-10-10
Convolutional neural networkBuilding a simple ConvNet architecture with some convolutional and pooling layers
Training ConvNet as feature extractor and then use it to extract features before feeding them into different models
Prediction and Confusion Matrix, Filter Visualization
Digit image classificationDeep learning 2016-10-10
LSTM Recurrent Neural NetworkAdagrad method for optimizations
weighted trainning
TheanoLearning and predicting sine waves Predict Time Sequence with LSTM2016-10-10
MxNETExtracts the features from the images that are used to train supervised classifier Trained on the Net Image dataseMxNet Library Image classificationNetwork of pre-trained neurons applied to the classification of images (French)2017-03-03
Feedforward NNFeed Forward Pass, Backward PropagationTheanoXNOR functionPractical Guide to implementing Neural Networks in Python (using Theano)2017-03-03
Feedforward NNSciKit Learn 0.18Predict type of tumor based on Breast Cancer Data Set - which has several features of tumors with a labeled class indicating whA Beginner Guide to Neural Networks with Python and SciKit Learn 0.18!2017-03-03
Feedforward NNGradient descent, backpropogationnumpyPredict test score based on how many hours we sleep and how many hours we study the night before.ARTIFICIAL NEURAL NETWORK (ANN) - INTRODUCTION2017-03-03
Feedforward NNDifferent types of neural networks are considerednumpy, Keras, TheanoImage Classification of MNIST imagesARTIFICIAL NEURAL NETWORK (ANN) 9 - DEEP LEARNING II : IMAGE RECOGNITION (IMAGE CLASSIFICATION) 2017-03-03
neural network library for pythonInterface to use train algorithms form scipy.optimize
Flexible network configurations and learning algorithms.
You may change: train, error, initialization and activation functions Unlimited number of neural layers and number of neurons in layers
Variety of supported types of Artificial Neural Network and learning algorithms
neurolab Different types of neural networks can be createdneurolab 0.3.52017-03-03
Generative Adversarial Networks (GAN)GAN has two competing neural network models:
Generator takes noise as input and generates samples.
Discriminator receives samples from both the generator and the training data, and has to be able to distinguish between the two sources.
tensorflowLearn to create data that is similar to data that we give themAn introduction to Generative Adversarial Networks (with code in TensorFlow)2017-05-05
Feedforward NNRectifier (relu) activation function on the first two layers and the sigmoid function in the output layer

gradient descent algorithm ?adam?
KerasBinary classification problem (onset of diabetes as 1 or not as 0)Develop Your First Neural Network in Python With Keras Step-By-Step2017-05-05
Spiking Neural Netorks (SNN)The site has tutorial with math explanationp y l a b
s c i p y . s p a r s e
Spiking Neural Netorks (SNN)Includes the modified learning and prediction rules which could be realised on hardware and are enegry efficient

Spike-Time Dependent Plasticity (STDP) algorithm is used to train the network.
numpyClassificationPure python implementation of SNN2017-05-05
Spiking Neural Netorks (SNN)open sourcebrian2Simulator for spiking neural networksThe Brian spiking neural network simulator2017-05-05
Spiking Neural Netorks (SNN)Different Spiking Neuron Models: Reflex neuron model, Habituation, Positive feedback neuron response and many othersDifferent Spiking Neuron ModelsSpiking Neural Netorks (SNN)2017-05-05
Self-Organising Maps (SOM)The input data is randomly initialized 3D colours

Normalization is included
Dimension reduction, Converting 3D colors into 2D MapSelf-Organising Maps: In Depth2017-05-05
Convolutional Neural Networktflearn - Deep learning library featuring a higher-level API for TensorFlow.tflearnObjects recognition in images using deep learningMachine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks2017-05-05
TensorFlow Neural Network3 layer deep neural networktensorflowImage classification of MNIST images (set of 28x28 pixel grayscale images which represent hand-written digits)Python TensorFlow Tutorial - Build a Neural Network2017-05-05
Feedforward NNscaling, one hidden layersklearnImage classification of MNIST images (set of 28x28 pixel grayscale images which represent hand-written digits)Neural Networks Tutorial - A Pathway to Deep Learning2017-05-05
Keras Neural NetworkRectifier activation in the hidden layer
ADAM gradient descent optimization algorithm with a logarithmic loss function
Multi-class classification problems for Iris datasetMulti-Class Classification Tutorial with the Keras Deep Learning Library2017-05-05
Echo Recurrent Neural NetworkVisualization
input is a random binary vecto
the output is the ?echo? of the input, shifted echo_step steps to the right
tensorflowEcho-RNN that remembers the input data and then echoes it after a few time-stepsHow to Build a Recurrent Neural Network in TensorFlow2017-05-05
LSTM Recurrent Neural NetworkMultilayered LSTM
deep architecture
tensorflowTensorFlow Multilayer-LSTMUsing the Multilayered LSTM API in TensorFlow2017-05-05
Convolutional Neural NetworkReLU node activations
softmax classification layer to output the 10 digit probabilities
tensorflowDigit Image classification of MNISTConvolutional Neural Networks Tutorial in TensorFlow2017-10-28
LSTM Recurrent Neural NetworkNetwork structure: 1 input layer (consisting of a sequence of size 50) which feeds into an LSTM layer with 50 neurons, that in turn feeds into another LSTM layer with 100 neurons which then feeds into a fully connected normal layer of 1 neuron with a linear activation function which will be used to give the prediction of the next time stepKerasLSTM NEURAL NETWORK FOR TIME SERIES PREDICTIONLSTM NEURAL NETWORK FOR TIME SERIES PREDICTION2017-10-28
Convolutional Neural NetworkShowing also how to download trained model from the community in the Caffe Model Zoo and use itcaffeimage classificationDeep learning tutorial on Caffe technology : basic commands, Python and C++ code.2017-10-28
Convolutional Neural NetworkConvolutional Neural Network with caffecaffeimage classificationDeep Learning With Caffe In Python - Part I: Defining A Layer2017-10-28
CNN, RNN2 stacked LSTMkerasfinancial time series forecasting, stock data forecasting Neural networks for algorithmic trading. Part One???Simple time series forecasting2017-10-28
Convolutional Neural Networktwo-stream-cnnensorflow (framework of choice) Gensim (nlp library foREDICTING SOCIAL MATCHES IN LINKEDIN-DATATWO STREAM CONVOLUTIONAL NETWORK FOR PREDICTING SOCIAL MATCHES IN LINKEDIN-DATA2017-10-28
Convolutional Neural NetworkConvolutional layer is added to the Walk-Forward Analysis.CNTKTime SeriesConvolutional Neural Network for Time Series2017-10-28
Convolutional Neural Network2D convolutional layer, 2D max pooling layerkerasTo classify the MNIST handwritten digit datasetKeras tutorial - build a convolutional neural network in 11 lines2017-10-28
Convolutional Neural NetworkTensorFlow Library. The post includes example of TensorFlow NN and CNNTensorFlowTo classify the MNIST handwritten digit datasetFirst steps with TensorFlow using Python2017-10-28
LSTM Recurrent Neural Networkmultilabel classificationTensorflowTime series classificationMultilabel-timeseries-classification-with-LSTM2017-10-28
LSTM Recurrent Neural NetworkKerasTime Series PredictionTime Series Analysis using Recurrent Neural Networks LSTM2017-10-28
CNNTensorFlowHuman Activity Recognition Implementing a CNN for Human Activity Recognition in Tensorflow2017-10-28
RNNN/ASimple toy exampleAnyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN)2017-10-28
CNN2D also includedKerasTime Series predictionExample of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.2017-10-28
RCNNLasagneThis project provides the solution of team daheimao for the Kaggle Grasp-and-Lift EEG Detection Competition.Kaggle Grasp-and-Lift EEG Detection Competition2017-10-28
Generative Adversarial Networks (GAN)The dataset of images is used (black and white)Keras
Generate digits by training a GAN on Identify the Digits datasetIntroductory guide to Generative Adversarial Networks (GANs) and their promise!2017-10-28
Multi-Layer Perceptron2 hidden layers neuronstensorflowusing the MNIST database of handwritten digitsaymericdamien/TensorFlow-Examples2017-10-28
Multi-Layer Perceptronvariation of diffent hyperparameters is exploredKerasTime Series Forecasting (Sales Data)Exploratory Configuration of a Multilayer Perceptron Network for Time Series Forecasting2017-10-28
Multi-Layer PerceptronIn this post a multi-layer perceptron (MLP) class based on the TensorFlow library is discussed.TensorFlowStock Market PredictionStock Market Prediction Using Multi-Layer Perceptrons With TensorFlow2017-10-28
Multi-Layer PerceptronTensorflow vs Theano BenchmarkTheano
Multi-Layer Perceptron Networks in Theano and TensorFlow: An Implementation and Benchmark2017-10-28
CNNThere is also great tutorialkerasImage classificationArchitecture of Convolutional Neural Networks (CNNs) demystified2017-10-28
RNNThe dataset consists of thousands of five-sentence stories (dataset is from ROCStories)kerasThe task is to predict the final sentence in each storyA simple recurrent neural network language model with Keras2017-10-28
Keras Neural Networkmulti-layer perceptrons NNkerasClassification - Predicting Wine Types: Red or WhiteKeras Tutorial: Deep Learning in Python2017-10-28
TensorFlowsparse softmax cross entropy between logits and labelsTensorFlowTraffic sign visualizationTensorFlow Tutorial For Beginners2017-10-28
TensorFlowFeed-Forward Neural Network (FFNN)TensorFlowBinary classification problem to classify colors into either red or blue based on the three RGB color channelsTensorFlow: Building Feed-Forward Neural Networks Step-by-Step2017-10-28
CNN The project is using FER-2013 Faces Database, a set of 28,709 pictures of people displaying 7 emotional expressions (angry, disgusted, fearful, happy, sad, surprised and neutral). 67% accuracy is reportedTensorFlowRecognition of emotions from imagesEmotion recognition using DNN with tensorflow, mood recognition using convolutional neural network2018-01-05
LSTMThe project is using One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling for data input to NN. For misspelled words artificial noise was usedkerasSpelling correctionDeep Spelling Rethinking spelling correction in the 21st?century2018-01-05
RNNBuild a Language Model using a Recurrent Neural Network. For the sentence of m words a language model allows to predict the proRecurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy and Theano2018-01-05
LSTMDual encoder LSTMretrieval-based neural network model that can assign scores to potential responses given a conversation context.Deep Learning for Chatbots, Implementing a Retrieval-Based Model in Tensorflow2018-01-05
CNNText ClassificationImplementing a CNN for Text Classification in TensorFlow2018-01-05
CNN3-layered convolution neural network with 2 dense layers. Layers: Embeddings, Convolution1D, Flatten, Dropout, Dens Accuracy 87%kerasSentiment analysisHow to implement Sentiment Analysis using word embedding and Convolutional Neural Networks on Keras.2018-01-05
CNNEach word is represented by an embedded vectorkerasShort Text Categorization using Deep Neural Networks and Word-Embedding ModelsShort Text Categorization using Deep Neural Networks and Word-Embedding?Models2018-01-05
CNNNeural Network is combined with reinforcement learning for game developmentTensorFlowGame DevelopmentUsing Machine Learning Agents in a real game: a beginner guide2018-01-05
CNNLeNet modelkerashandwritten digit classification for MNIST datasetVisualizing the Convolutional Filters of the LeNet Model2018-01-05
CNNImageNet modelkerasimage classificationHow convolutional neural networks see the world2018-01-05
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