A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
machine-learning
deep-learning
data-science
python
automated-machine-learning
natural-language-processing
hyperparameter-optimization
automl
jupyter-notebook
timeseries-forecasting
tuning
classification
finetuning
hyperparam
natural-language-generation
random-forest
regression
scikit-learn
tabular-data
Обновлено 2024-09-06 07:00:13 +03:00
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
machine-learning
deep-learning
data-science
python
tensorflow
mlops
pytorch
hyperparameter-optimization
hyperparameter-tuning
machine-learning-algorithms
model-compression
nas
neural-architecture-search
neural-network
automated-machine-learning
automl
bayesian-optimization
deep-neural-network
distributed
feature-engineering
Обновлено 2024-07-03 13:54:08 +03:00
Self regulation and auto-tuning for distributed system
Обновлено 2023-06-15 23:10:25 +03:00
End-to-End recipes for pre-training and fine-tuning BERT using Azure Machine Learning Service
nlp
pytorch
azure-machine-learning
language-model
bert
tuning
finetuning
pretraining
azureml-bert
bert-model
pretrained-models
Обновлено 2023-06-12 21:59:00 +03:00
The DSB benchmark is designed for evaluating both workloaddriven and traditional database systems on modern decision support workloads. DSB is adapted from the widely-used industrialstandard TPC-DS benchmark. It enhances the TPC-DS benchmark with complex data distribution and challenging yet semantically meaningful query templates. DSB also introduces configurable and dynamic workloads to assess the adaptability of database systems. Since workload-driven and traditional database systems have different performance dimensions, including the additional resources required for tuning and maintaining the systems, we provide guidelines on evaluation methodology and metrics to report.
Обновлено 2023-05-30 11:30:36 +03:00
Time Series Forecasting Best Practices & Examples
machine-learning
python
deep-learning
artificial-intelligence
r
best-practices
jupyter-notebook
automl
lightgbm
hyperparameter-tuning
model-deployment
prophet
retail
tidyverse
time-series
azure-ml
demand-forecasting
dilated-cnn
forecasting
Обновлено 2023-05-01 00:54:37 +03:00
Code for automatically tuning quantum dots
Обновлено 2023-01-31 13:22:19 +03:00
performance
performance-testing
dsl
haskell
model-checking
modeling
performance-analysis
performance-tuning
queueing-theory
Обновлено 2020-12-16 18:50:06 +03:00
Peregrine is a workload optimization platform for cloud query engines. The goal of Peregrine is three-fold: 1. make it easier to ingest and analyze query workload telemetry into a common engine-agnostic representation, 2. help developers to quickly build workload optimization applications to reduce overall costs and improve operational efficiency, and 3. providing better experience to the customers in the form of workload insights, actionable recommendations, and self-tuning capabilities.
Обновлено 2020-08-31 07:53:14 +03:00
Example of using HyperDrive to tune a regular ML learner.
Обновлено 2020-04-08 00:51:45 +03:00
Hyperparameter Tuning for Deep Learning
Обновлено 2020-02-05 03:19:52 +03:00