Pinned Repositories
anomalydetector
SR-CNN
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
bert
TensorFlow code and pre-trained models for BERT
BERT-KPE
causalml
Uplift modeling and causal inference with machine learning algorithms
darts
A python library for easy manipulation and forecasting of time series.
DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
eng-edu
subhamkhemka's Repositories
subhamkhemka/anomalydetector
SR-CNN
subhamkhemka/BayesianOptimization
A Python implementation of global optimization with gaussian processes.
subhamkhemka/bert
TensorFlow code and pre-trained models for BERT
subhamkhemka/BERT-KPE
subhamkhemka/causalml
Uplift modeling and causal inference with machine learning algorithms
subhamkhemka/darts
A python library for easy manipulation and forecasting of time series.
subhamkhemka/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
subhamkhemka/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
subhamkhemka/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
subhamkhemka/eng-edu
subhamkhemka/forecasting
Time Series Forecasting Best Practices & Examples
subhamkhemka/hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
subhamkhemka/kaggle-solutions
🏅 Collection of Kaggle Solutions and Ideas 🏅
subhamkhemka/keda
KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
subhamkhemka/luminol
Anomaly Detection and Correlation library
subhamkhemka/neural_prophet
NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch
subhamkhemka/nlp-recipes
Natural Language Processing Best Practices & Examples
subhamkhemka/nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
subhamkhemka/optuna
A hyperparameter optimization framework
subhamkhemka/orbit
Bayesian forecasting with object-oriented design and probabilistic models under the hood.
subhamkhemka/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
subhamkhemka/pyod
A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
subhamkhemka/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
subhamkhemka/recommenders
Best Practices on Recommendation Systems
subhamkhemka/sktime
A unified framework for machine learning with time series
subhamkhemka/tf-quant-finance
High-performance TensorFlow library for quantitative finance.
subhamkhemka/Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors.
subhamkhemka/transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
subhamkhemka/tsfresh
Automatic extraction of relevant features from time series:
subhamkhemka/vaex
Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀