There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Text-Classification-LSTMs-PyTorch
The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a Tweets dataset provided by Kaggle.Text-Classification-CNN-PyTorch
The aim of this repository is to show a baseline model for text classification through convolutional neural networks in the PyTorch framework. The architecture implemented in this model was inspired by the one proposed in the paper: Convolutional Neural Networks for Sentence Classification.Text-Generation-BiLSTM-PyTorch
In this repository you will find an end-to-end model for text generation by implementing a Bi-LSTM-LSTM based model with PyTorch's LSTMCells.Kubeflow_Pipelines
This repository aims to develop a step-by-step tutorial on how to build a Kubeflow Pipeline from scratch in your local machine.Stacking-Blending-Voting-Ensembles
This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.SKORCH-PyTorch-Wrapper
This repository shows an example of the usability of SKORCH to train a PyTorch model making use of different capabilities of the scikit-learn framework.SHAP-Classification-example
This repository contains an example of how to implement the shap library to interpret a machine learning model.VGAE-PyTorch
This repository shows an implementation of the VGAE based model with PyTorch.TPOT-Optimal-Pipeline-Searching
This repository contains an implementation of TPOT for obtaining optimal pipelines with the use of genetic algorithms.Tracking-ML-model-MLflow
This repository shows the use of MLflow to track parameters, metrics and artifacts of a pipeline on a machine learning model.PolynomialCurveFitting
This code shows the implementation of polynomial curve fitting and the regularization over the parameters. In this example we are trying to fit the curve generate by the function sin(2pix), where "x" is a vector of values generated randomly under a normal distribution.SpeechRecognition
This repository contains the implementation of an Automatic Speech Recognition system in python, using a client-server architecture with Web Sockets.ONNX-PyTorch-TF-Caffe2
This repository shows an example of how to use the ONNX standard to interoperate between different frameworks. In this example, we train a model with PyTorch and make predictions with Tensorflow, ONNX Runtime, and Caffe2.Optuna-Sklearn-PyTorch
AuthorVerificiation
This repository shows up a siamese arquitectue proposed to solve the problem of author verification particularly the problem about given a pair of documents decide if both are from the same author or not based on their writting style. The siamese arquitecture is composed by an assemble of two convolutional layers and a LSTM recurrent neurnal net followed by a euclidean distance.FeatureSelection_-_RandomForest
# Feature Selection & Random Forest-based Model In this kernel I will develop a solution by first, selecting the most relevant features and then applying a random forest to solve the classification problemPyTorch-Lightning
This repository shows a couple of examples to start using PyTorch Lightning right away. PyTorch Lightning provides several functionalities that allow to organize in a flexible, clean and understandable way each component of the training phase of a PyTorch modelMLFlow-example
Crimes
The purpose of this notebook is to show a deep analysis of the behavior of crimes happended in CDMX, MΓ©xico in years from 2014 to 2016.AnalyzingDocuments
This project shows up the algorithm k-means implemented to cluster documents from the contest PAN CLEF 2O16 where the topics of the documentes are reviews and novels.Analysis_Taxi_behavior_NY
In this notebook I show you an analysis of taxis behavior in September, 2015 NY. The idea of this work is to find and show you insights as well as some visualizations to understand in a better way the analysis.Love Open Source and this site? Check out how you can help us