sorawitj

sorawitj's Stars

• dennybritz/reinforcement-learning

  Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

  Language:Jupyter Notebook20.4k8601556k

• microsoft/LightGBM

  A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  Language:C++16.5k4343.4k3.8k

• ydataai/ydata-profiling

  1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

  Language:Python12.4k1528011.7k

• taki0112/Densenet-Tensorflow

  Simple Tensorflow implementation of Densenet using Cifar10, MNIST

  Language:Python5042624196

• mdda/deep-learning-workshop

  Deep Learning Workshop : Including a VirtualBox VM with pre-configured Jupyter, Tensorflow, PyTorch, models and data

  Language:Jupyter Notebook470314121

• acbraith/CSML_notes

  UCL MSc Computational Statistics and Machine Learning Revision Notes

  27913059

• ijmbarr/causalgraphicalmodels

  Causal Graphical Models in Python

  Language:Jupyter Notebook23913744

• jessicayung/machine-learning-flashcards

  Machine learning flashcards

  Language:TeX21511442

• cheng6076/Variational-LSTM-Autoencoder

  Variational Seq2Seq model

  Language:Lua1339523

• sanghoon/tf-exercise-gan

  Tensorflow implementation of different GANs and their comparisions

  Language:Python11316032

• pekaalto/sc2aibot

  Implementing reinforcement-learning algorithms for pysc2 -environment

  Language:Python899414

• adith387/slates_semisynth_expts

  Semi-synthetic experiments to test several approaches for off-policy evaluation and optimization of slate recommenders.

  Language:Python434010

• juangamella/icp

  Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant prediction: identification and confidence intervals" by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.

  Language:Python18201

• gianlucatruda/warfit-learn

  A machine learning toolkit for reproducible research in anticoagulant dose estimation.

  Language:Python11594

• CausalML/continuous-policy-learning

  Language:Jupyter Notebook10244

• klasnja/HeartStepsV1

  Data from the HeartSteps V1 study

  91

• LucasKook/tramicp

  Model-based causal feature selection for general response types

  Language:R60

• simzim96/AnchorRegression

  Language:R41