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Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocationbabble-rnn is a research project in the use of machine learning to generate new speech by modelling human speech audio, without any intermediate text or word representations. The idea is to learn to speak through imitation, much like a baby might. The goal is to generate a babbling audio output that emulates the speech patterns of the original speaker, ideally incorporating real words into the output. The implementation is based on Keras / Theano, generating an LSTM RNN; and Codec 2, an open source speech audio compression algorithm. The resulting models have learned the most common audio sequences of a 'performer', and can generate a probable babbling audio sequence when provided a seed sequence.

keras keras-neural-networks jupyter-notebook keras-modelsYou can fix it by upgrading Tensorflow. The video captioning model here uses Mean Pooled ResNet50 features of video frames along with Object, Action and Attribute tags predicted by a simple feedforward network.

deep-learning computer-vision natural-language-processing keras keras-models deep-video-analytics video-captioning video-to-textThis is ready to use, end-to-end sample solution for the currently running Kaggle Cdiscount challenge. It involves data loading and augmentation, model training (many different architectures), ensembling and submit generator.

data-science machine-learning deep-learning kaggle kaggle-challenge neptune python3 keras keras-models keras-implementations competitionExamples and tutorials to the steppy library. Steppy is a lightweight, open-source, Python library for fast and reproducible experimentation.

pipeline data-science machine-learning deep-learning steps nlp reproducible-research reproducibility steppy tutorial steppy-library python3 keras keras-models keras-implementations pytorch pytorch-implmention pytorch-modelsSet of tools to make your work with Steppy faster and more effective. Steppy is a lightweight, open-source, Python library for fast and reproducible experimentation.

pipeline data-science machine-learning deep-learning steps nlp reproducible-research reproducibility steppy steppy-toolkit python3 keras tensorflow tensorflow-models keras-models pytorch pytorch-models open-source pipeline-frameworkGestureAI is a RNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures(Circl, Rectangle, Triangle, Cross and the other). This dataset of hand-motion drawing 5 figures is sequences of 3-axis accelerations captured by iPhone. Example to implement RNN in Keras gets 90.8% accuracy by Cross-validation. You can use direct links to download the dataset.

keras keras-tensorflow keras-models tensorflow python-2 rnn coreml ios deep-learning deep-neural-networks machine-learning recurrent-nerural-network demo datasetIn the Jupyter notebook for this repository, I begin by calculating the bottleneck features for the CIFAR-10 dataset. These features are then visualized with a Barnes-Hut implementation of t-SNE, which is the fastest t-SNE implementation to date.

keras keras-neural-networks keras-models keras-classification-models keras-visualization images image-classification classification classifier classification-algorithm cnn cnn-keras cnn-model cnn-architecture convolutional-neural-networks convolutional-networks tsne tsne-algorithm visualization transfer-learningThe original Matlab implementation and paper (for AlexNet, GoogLeNet, and VGG16) can be found here. A Keras implementation of VGG-CAM can be found here. This implementation is written in Keras and uses ResNet-50, which was not explored in the original paper.

keras cnn resnet-50 resnet localization cnn-keras cnn-model cnns localisation image-analysis classification image-classification keras-neural-networks keras-tensorflow keras-visualization keras-models keras-classification-modelsSee also the upcoming webinar (10 Oct 2018), in which we walk trough the code.

keras pytorch keras-tutorials pytorch-tutorials alien predator keras-neural-networks keras-models keras-classification-models pytorch-tutorial pytorch-cnn pytorch-implementationA set of models which allow easy creation of Keras models to be used for classification purposes. Also contains modules which offer implementations of recent papers. An implementation of "SparseNets" from the paper Sparsely Connected Convolutional Networks in Keras 2.0+.

residual-networks keras-models keras-classification-models deep-learning kerasA pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. You can see visualizations of each model's network architecture by using Netron.

audio machine-learning caffe neural-network mxnet tensorflow keras python3 pytorch speech-recognition speech-to-text keras-models audio-processing tensorflow-models pre-trained keras-tensorflow pytorch-models pre-training pre-trained-modelSearch for neural networks architecture & hyper parameters with genetic algorithms. It is built on top of Keras+Tensorflow to build/train/evaluate the models, and uses DEAP for the genetic algorithms. Then you define the parameters of the training. If you specify only the name of the optimizer to use, and no parameters, random parameters will be tested during the experiment, hopefully converging to optimal parameters. You can choose to stop the training after a fixed number of epochs, or when the accuracy of the model evaluated stops increasing.

deep-learning tensorflow genetic-algorithm hyperparameters keras-modelsWe will host the model file. Do a pull request with your updates and a link to your model. During review we will download and host your model on our S3 so you can update your pull request URL to one that we will host. You simply need to find a way to get your model in our hands temporarily and we'll take on the hosting for the repo. Update the README.md to have all the information on your provided model. Then add a folder in the /info section that includes credit, examples, and more friendly information on your trained keras model.

machine-learning model-zoo models keras keras-models keras-tensorflow
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