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Apache MXNet Tutorial – Learn Apache MXNet Apache MXNet Tutorial Apache MXNet Tutorial – Learn MXNet to work on Deep Neural Networks with detailed examples and downloadable materials. What is Apache MXNet ? Apache MXNet is a Deep Learning framework. It helps in training and deploying deep neural networks efficiently. The library is so lightweight and it offers flexibility with its support to both imperative and symbolic programming. MXNet is an Artificial Intelligence Engine like TensorFlow, Caffe, Torch, Theano, CNTK, Keras etc. It is an opensource and freeware from Apache Software Foundation. Features of MXNet Following are some of the interesting features of MXNet : Scalability Scalability is one of the design aspects of MXNet. It is scalable to multiple GPUs and multiple machines. Programmability MXNet offers simple syntax and support for multiple languages (Python, C++, Scala, Julia, Matlab, Perl and JavaScript). Portability MXNet can build highly efficient models for smart phones and IoT applications. And MXNet could run on Raspberry Pi. Performance MXNet can achieve near linear scaling of efficiency with number of GPUs (nearly across hundreds). Model Support It provides state of the art Model support for CNN (Convolutional Neural Networks) anLearn Apache MXNetd LSTM (Long Short-Term Memory). Ecosystem There is an open and vibrant community from industry and enthusiasts. Apache Big Data Tutorials ⊩ Learn Apache Hadoop ⊩ Learn Apache Spark ⊩ Learn Apache Flink ⊩ Learn Apache Beam ⊩ Learn Apache MXNet Apache Database Projects ⊩ Apache CouchDB Tutorial Apache Other Projects ⊩ Apache Tomcat Tutorial ⊩ Apache PDFBox Tutorial ⊩ Learn Apache OpenNLP.

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Apache Flink™: Stream and Batch Processing in a Single Engine
Apache Flink™: Stream and Batch Processing in a Single Engine Paris Carboney Stephan Ewenz Seif Haridiy Asterios Katsifodimos* Volker Markl* Kostas Tzoumasz yKTH & SICS Sweden zdata Artisans *TU Berlin & DFKI parisc,[email protected] fi[email protected] fi[email protected] Abstract Apache Flink1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continu- ous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this paper, we present Flink’s architecture and expand on how a (seemingly diverse) set of use cases can be unified under a single execution model. 1 Introduction Data-stream processing (e.g., as exemplified by complex event processing systems) and static (batch) data pro- cessing (e.g., as exemplified by MPP databases and Hadoop) were traditionally considered as two very different types of applications. They were programmed using different programming models and APIs, and were exe- cuted by different systems (e.g., dedicated streaming systems such as Apache Storm, IBM Infosphere Streams, Microsoft StreamInsight, or Streambase versus relational databases or execution engines for Hadoop, including Apache Spark and Apache Drill). Traditionally, batch data analysis made up for the lion’s share of the use cases, data sizes, and market, while streaming data analysis mostly served specialized applications. It is becoming more and more apparent, however, that a huge number of today’s large-scale data processing use cases handle data that is, in reality, produced continuously over time.
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Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions
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