A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Deep Learning API and Server in C++11 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
A container for Deep Learning with Python 3
Data Science: AMD/OpenCL GPU Deep Learning: Setup Python + Caffe/XGBoost + 1.7x RAM
This project aims to build effective stock selection models based on machine learning tools such as Keras, Tensorflow, XGBoost, etc., for Chinese stock market participants.
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
This is a light-weight machine learning framework for text classification in R. While Keras, XGBoost or Ranger (RF) learn the objective, deeplyr takes core of model logging, benchmarking and also optimizing over hyperparameter spaces.
Designed a classifier using various models such as Naïve bayes, SVM, NN, Deep learning, LGBM, XGBoost, etc. Performed pre-processing of data and applied k-fold cross-validation. Secured Top 100 position on Kaggle competition leaderboard with an accuracy of 69.62%.
Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.