spark
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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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Dec 6, 2019 - Python
Learn and understand Docker technologies, with real DevOps practice!
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Dec 6, 2019 - Go
Eclipse Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java/Scala with GPUs + Spark
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Dec 6, 2019 - Java
汇总java生态圈常用技术框架、开源中间件,系统架构、数据库、大公司架构案例、常用三方类库、项目管理、线上问题排查、个人成长、思考等知识
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Dec 6, 2019
Hey Cube.Js team!
I get the following yarn warnings when installing dependencies. Can these be fixed? Thank you.
warning @cubejs-backend/server-core > @cubejs-backend/schema-compiler > joi@14.3.1: This module has moved and is now available at @hapi/joi. Please update your dependencies as this version is no longer maintained an may contain bugs and security issues.
warning @cubejs-backe
Is there a doc of an example angle-submit for training word2vec? Also I couldn't find a config_details.md for the word2vec model. Thanks for help in advance!
if I understood it corretly from README.MD, we can install like this:
$ git clone https://github.com/donnemartin/dev-setup.git && cd dev-setup
$ ./.dots bootstrap osxprep brew osx
and later when we need datastores, we run
$ cd ~/dev-setup
$ ./.dots datastores
I understand that bootstrap copies the dot files to the home directory, such as .bash_profile and .exports.
but
After reading through https://thingsboard.io/docs/user-guide/install/windows/, I fell over the issue that my Thingsboard did tell me that the relations needed do not exist. What did I do? Installed Thingsboard, ran it and afterwards changed the database to Postgres. After checking the issues, I found this one: thingsboard/thingsboard#1021.
The request is to add the inf
Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Dec 6, 2019 - Java
Alluxio, data orchestration for analytics and machine learning in the cloud
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Dec 6, 2019 - Java
PipelineAI Kubeflow Distribution
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Dec 5, 2019 - Jsonnet
flink learning blog. http://www.54tianzhisheng.cn 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例,还有 Flink 落地应用的大型项目案例分享。
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Dec 6, 2019 - Java
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
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Dec 6, 2019 - Python
Spark 2.3 officially support run on kubernetes. While our guide of "Run on Kubernetes" is still based on a special version of Spark 2.2, which is out of date. We need to:
- update that document to Spark 2.3
- release the corresponding docker images.
Interactive and Reactive Data Science using Scala and Spark.
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Dec 6, 2019 - JavaScript
酷玩 Spark: Spark 源代码解析、Spark 类库等
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Dec 5, 2019 - Scala
In this section of the Quickstart tutorial, there is a mismatch between the Scala and Java code examples.
The Java example reads:
deltaTable.as("events")
.merge(
newData,
"oldData.id = newData.id")
.whenMatched()
...
but "events" should be "oldData".
I think it'd be worth adding a new tokenizer for paths that emulate the carbonblack queries. I feel like they have put a lot of thought in this schema and over time using CB I have come to appreciate it's strengths.
Snippet from their documentation here
"C:\Windows\system32\rundll32.exe" /d
I am trying to explain the predictions made by my XGboost model using MMLSparks Lime package for scala. This is my first time using LIME library, I am able to perform a fit operation on the dataset and when I am trying to perform the transform operation, the program stops with an exception, "Caused by: java.lang.ClassCastException: org.apache.spark.ml.linalg.SparseVector cannot be cast to org.apac
Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.
Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
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Dec 5, 2019 - R
Fast, Scientific and Numerical Computing for the JVM (NDArrays)
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Dec 6, 2019 - Java
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麻烦指导,谢谢
版本和配置信息