인공지능과 통계학
R2026b병렬 연산으로 통계, 머신러닝 및 딥러닝 애플리케이션 가속화
병렬 연산을 활용하여 통계적 계산과 머신러닝 및 딥러닝 모델을 이용한 훈련 및 예측 워크플로를 가속화합니다.
앱
도움말 항목
통계학 및 기계 학습
- Analyze and Model Data on GPU (Statistics and Machine Learning Toolbox)
Accelerate your code by using GPU array input arguments. - Accelerate Linear Model Fitting on GPU (Statistics and Machine Learning Toolbox)
This example shows how you can accelerate regression model fitting by running functions on a graphical processing unit (GPU).
딥러닝
- Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud (Deep Learning Toolbox)
Explore options for deep learning with MATLAB® in parallel and using multiple GPUs, locally or in the cloud. - Run Experiments in Parallel (Deep Learning Toolbox)
Run multiple simultaneous trials or one trial at a time on multiple workers. - Train Deep Learning Networks in Parallel (Deep Learning Toolbox)
This example shows how to run multiple deep learning experiments on your local machine.
관련 정보
gpuArray를 지원하는 함수 (Statistics and Machine Learning Toolbox)- 자동 병렬 연산을 지원하는 함수 (Statistics and Machine Learning Toolbox)
gpuArray를 지원하는 함수 (Deep Learning Toolbox)
추천 예제
Train Classification Ensemble in Parallel
Train a bagged ensemble in parallel reproducibly.
Wide Data via Lasso and Parallel Computing
Identify important predictors using lasso and cross-validation.
Use Parallel Processing for Regression TreeBagger Workflow
Speed up computation by running TreeBagger in parallel.
자동 병렬화 기능을 사용하여 클라우드의 신경망 훈련시키기
이 예제에서는 병렬 훈련에 대한 MATLAB® 자동 지원을 사용하여 컨벌루션 신경망을 훈련시키는 방법을 보여줍니다.
Train Network Using Automatic Multi-GPU Support
Use multiple GPUs on your local machine for deep learning training using automatic parallel support.
Send Deep Learning Batch Job to Cluster
Train a deep neural network on a cluster while you continue working in MATLAB® or when you close it.
