1. Concept 1.1. Estimators The Estimators have a method called fit() which secures and trains a piece of data to such application. 1.2. Transformers The Transformer is generally the result of a fitting process and applies changes to the the target dataset. 1.3. Pipelines Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a Machine Learning task. spark =

OpenMMLab 在Github上不是一个单独项目，除了大家所熟知的 Github 上万 star 目标检测库 MMDetection，还有其他方向的代码库和数据集,目前Github

Apart from real-time and batch processing, Apache Spark supports interactive queries and iterative algorithms. Using PySpark, you can work with RDDs in Python programming language also. It is because of a library called Py4j that they are able to achieve this. Resilient Distributed Dataset (RDD): RDD is an immutable (read-only), fundamental collection of elements or items that can be operated on many devices at the same time (parallel processing). Each

1.代码片段 1.1.导入配置 import torch import torch.nn as nn import torchvision print(torch.__version__) print(torch.version.cuda) print(torch.backends.cudnn.version()) print(torch.cuda.get_device_name(0)) 1.2. 显卡设置 # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #这只指定多张显卡 import os os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' #清除显存 torch.cuda.empty_cache() 1.3. Tensor 处理 tensor = torch.randn(3,4,5) print(tensor.type()) #

在以数据驱动的人工智能时代，本文用于平时学习或者阅读论文中所涉及到的开源数据集积累。dataset知识图谱 More:https://www.codetd.com/article/7219369 Dataset: https://www.cnblogs.com/xiaojianliu/p/9446358.html FaceScape 一个大规模高质量的3D人脸

Siamese Network 是一种神经网络的框架，用于评估两个输入样本的相似度，而不是具体的某种网络，就像seq2seq一样，具体实现上可以使用RNN也可以使用CN