DGL pip whl文件 dgl-1.1.1-cp38-cp38-win-amd64.whl

Deep Graph Library是一个python库，用于在现有的深度学习框架（例如PyTorch和MXNet）上轻松实现图神经网络模型。

YOLOv8预训练权重文件集合(YOLOv8n,YOLOv8s,YOLOv8m,YOLOv8l,YOLOv8x)

YOLOv8预训练权重文件集合(YOLOv8n,YOLOv8s,YOLOv8m,YOLOv8l,YOLOv8x) YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on the COCO dataset, while Classify models are pretrained on the ImageNet dataset. Model size (pixels) mAPval 50-95 Speed CPU ONNX (ms) Speed A100 TensorRT (ms) params (M) FLOPs (B) YOLOv8n 640 37.3 80.4 0.99 3.2 8.7 YOLOv8s 640 44.9 128.4 1.20 11.2 28.6 YOLOv8m 640 50.2 234.7 1.83 25.9 78.9 YOLOv8l 640 52.9 375.2 2.39 43.7 165.2 YOLOv8x 640 53

常见的tensorflow-gpu2.x缺失dll(cublas64-11.dll&cublasLt64-11.dll等)

>>> import tensorflow as tf >>> tf.config.list_physical_devices('GPU') 2023-06-09 22:23:25.593906: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found 2023-06-09 22:23:25.594619: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found 2023-06-09 22:23:25.595266: W tensorflow/stream_executor/platform

MIFS算法MATLAB实现

MIPS（Mean Inter-Point Squared Distance）是机器学习中常用的聚类算法之一，它通过计算聚类中所有数据点互相之间的距离来确定聚类中心。这里是MIPS算法的MATLAB实现过程

CUDA: Monte Carlo simulation

Monte Carlo simulation 蒙特卡洛实验CUDA实现 Monte Carlo simulation is used to determine the range of outcomes for a series of parameters, each of which has a probability distribution showing how likely each option is to happen. In this project, you will take a scenario and develop a Monte Carlo simulation of it, determining how likely a particular output is to happen. Clearly, this is very parallelizable -- it is the same computation being run on many permutations of the input parameters.

OpenMP: Monte Carlo Simulation Code

OpenMP蒙特卡洛实验代码 Monte Carlo simulation is used to determine the range of outcomes for a series of parameters, each of which has a probability distribution showing how likely each option is to happen. In this project, you will take a scenario and develop a Monte Carlo simulation of it, determining how likely a particular output is to happen. Clearly, this is very parallelizable -- it is the same computation being run on many permutations of the input parameters. You will run this with OpenMP,

Web3拍卖智能合约示例以及说明

Web3拍卖智能合约示例以及说明

气象研究必备pip库：netCDF4-1.5.8-cp37-cp37m-win-amd64

气象研究必备pip库：netCDF4-1.5.8-cp37-cp37m-win-amd64