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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.

  • Updated Oct 1, 2020
  • Python

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.

  • Updated Oct 28, 2020
  • Jupyter Notebook
datapythonista
datapythonista commented Oct 27, 2020

Our docs currently requires an Impala instance to be built. At least in the last tutorials (the first 3 were written recently and don't use it), and in the Impala docs.

It's not ideal to require Impala running for the docs. There are two things we can do to avoid this:

  • For generic (all backends) docs, we can simply use the SQLite backend, which doesn't require anything, not even dependencies

450+ AWS, Hadoop, Cloud, Kafka, Docker, Elasticsearch, RabbitMQ, Redis, HBase, Solr, Cassandra, ZooKeeper, HDFS, Yarn, Hive, Presto, Drill, Impala, Consul, Spark, Jenkins, Travis CI, Git, MySQL, Linux, DNS, Whois, SSL Certs, Yum Security Updates, Kubernetes, Cloudera etc...

  • Updated Oct 27, 2020
  • Python

50+ DockerHub public images for Docker & Kubernetes - Hadoop, Kafka, ZooKeeper, HBase, Cassandra, Solr, SolrCloud, Presto, Apache Drill, Nifi, Spark, Consul, Riak, and DevOps tools built various distros: Alpine, CentOS, Debian, Fedora, Ubuntu

  • Updated Oct 23, 2020
  • Shell

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